A Network View on Parkinson’s Disease Elsevier webinar 15 jan 2015
•
1 like•1,066 views
Professor D.Bonchev shows an in-depth look at how a systems biology approach was used to identify some of the critical aspects Parkinson's disease: molecular players, drug targets, and underlying biological processes.
Report
Share
Report
Share
Download to read offline
Recommended
Cause-effect relationships in medicine
This document discusses moving from structure-based drug discovery approaches to network-based approaches that consider biological complexity. It argues that current reductionist models cannot predict drug efficacy and effects since biological effects result from multiple perturbations of cellular networks, not just primary target interactions. The document then outlines key components of a network-based drug discovery approach, including constructing protein-protein interaction networks, identifying network-reachable proteins, and assessing information flow between proteins to better understand disease mechanisms and drug actions.
Reconstruction and analysis of cancerspecific Gene regulatory networks from G...
This document summarizes a study that reconstructed a cancer-specific gene regulatory network for prostate cancer from gene expression profiles. The researchers identified differentially expressed genes between cancer and normal tissue samples using statistical tests. They then computed correlations between gene pairs to identify regulatory relationships, focusing on highly correlated pairs. This resulted in a network of 29 genes and 55 regulatory relationships. The network was validated against biological databases and literature, and topological analysis identified some highly connected "hub" genes that may be potential drug targets.
Network Pharmacology Tri-Con 022212
Presentation at the Network Pharmacology session of Molecular Med Tri-Con 2012 Meeting San Francisco Feb 22, 2012.
Identification of novel potential anti cancer agents using network pharmacolo...
This document discusses using network pharmacology and computational modeling to identify novel potential anti-cancer agents. It describes how E-Therapeutics constructs disease networks and then uses its proprietary chemoinformatics tools to identify compounds that could impact those networks. One lead anti-cancer candidate, Dexanabinol, is highlighted which has passed Phase 1 trials. Experimental validation of compounds predicted to impact glioma networks identified over 50% as weakly active potential leads and 14 as highly active candidates, demonstrating the potential of this network pharmacology approach.
Novel network pharmacology methods for drug mechanism of action identificatio...
Novel network pharmacology methods for drug mechanism of action identification, pre-clinical drug screening and drug repositioning
NetBioSIG2014-Talk by David Amar
This document summarizes a study that integrated pathway and gene expression data from over 13,000 samples across 17 platforms to perform multi-label classification of 48 diseases. Pathway activity scores were calculated for each sample and used as features for classification, along with sample labels determined through manual dataset analysis. Classification was performed using multiple algorithms and validated through cross-validation and comparison to previous studies. Performance was improved over previous work, as shown by increased recall and precision. Relationships between diseases and pathways were also modeled in a network graph.
PGX Data Mining
This document discusses using hierarchical clustering algorithms to mine pharmacogenomic data from a clinical data warehouse containing both genomic and clinical trial data. It describes hierarchical clustering as an approach to analyze gene expression microarray data by grouping single profiles into clusters in a tree structure. Six main hierarchical clustering algorithms are outlined that differ in how they calculate distances between clusters. The goal of pharmacogenomic data mining is to discover knowledge that can identify the most effective and least toxic drugs for individuals based on their genetic makeup and disease.
Loss of Connectivity in Cancer Co-Expression Networks - PLoS ONE 9(1): e87075...
This document summarizes a study that analyzed gene expression data from five types of cancer (colorectal, lung, gastric, pancreatic, cervical) to identify genes with significantly different connectivity patterns between normal and tumor tissues. The study found that a loss of connectivity is a common feature of cancer networks compared to normal networks. Analyzing differentially connected genes can reveal new candidate cancer genes not identified by differential expression analysis alone. Integrating differential expression and differential connectivity data improves pathway enrichment analysis and provides insights into novel cancer-related pathways.
Recommended
Cause-effect relationships in medicine
This document discusses moving from structure-based drug discovery approaches to network-based approaches that consider biological complexity. It argues that current reductionist models cannot predict drug efficacy and effects since biological effects result from multiple perturbations of cellular networks, not just primary target interactions. The document then outlines key components of a network-based drug discovery approach, including constructing protein-protein interaction networks, identifying network-reachable proteins, and assessing information flow between proteins to better understand disease mechanisms and drug actions.
Reconstruction and analysis of cancerspecific Gene regulatory networks from G...
This document summarizes a study that reconstructed a cancer-specific gene regulatory network for prostate cancer from gene expression profiles. The researchers identified differentially expressed genes between cancer and normal tissue samples using statistical tests. They then computed correlations between gene pairs to identify regulatory relationships, focusing on highly correlated pairs. This resulted in a network of 29 genes and 55 regulatory relationships. The network was validated against biological databases and literature, and topological analysis identified some highly connected "hub" genes that may be potential drug targets.
Network Pharmacology Tri-Con 022212
Presentation at the Network Pharmacology session of Molecular Med Tri-Con 2012 Meeting San Francisco Feb 22, 2012.
Identification of novel potential anti cancer agents using network pharmacolo...
This document discusses using network pharmacology and computational modeling to identify novel potential anti-cancer agents. It describes how E-Therapeutics constructs disease networks and then uses its proprietary chemoinformatics tools to identify compounds that could impact those networks. One lead anti-cancer candidate, Dexanabinol, is highlighted which has passed Phase 1 trials. Experimental validation of compounds predicted to impact glioma networks identified over 50% as weakly active potential leads and 14 as highly active candidates, demonstrating the potential of this network pharmacology approach.
Novel network pharmacology methods for drug mechanism of action identificatio...
Novel network pharmacology methods for drug mechanism of action identification, pre-clinical drug screening and drug repositioning
NetBioSIG2014-Talk by David Amar
This document summarizes a study that integrated pathway and gene expression data from over 13,000 samples across 17 platforms to perform multi-label classification of 48 diseases. Pathway activity scores were calculated for each sample and used as features for classification, along with sample labels determined through manual dataset analysis. Classification was performed using multiple algorithms and validated through cross-validation and comparison to previous studies. Performance was improved over previous work, as shown by increased recall and precision. Relationships between diseases and pathways were also modeled in a network graph.
PGX Data Mining
This document discusses using hierarchical clustering algorithms to mine pharmacogenomic data from a clinical data warehouse containing both genomic and clinical trial data. It describes hierarchical clustering as an approach to analyze gene expression microarray data by grouping single profiles into clusters in a tree structure. Six main hierarchical clustering algorithms are outlined that differ in how they calculate distances between clusters. The goal of pharmacogenomic data mining is to discover knowledge that can identify the most effective and least toxic drugs for individuals based on their genetic makeup and disease.
Loss of Connectivity in Cancer Co-Expression Networks - PLoS ONE 9(1): e87075...
This document summarizes a study that analyzed gene expression data from five types of cancer (colorectal, lung, gastric, pancreatic, cervical) to identify genes with significantly different connectivity patterns between normal and tumor tissues. The study found that a loss of connectivity is a common feature of cancer networks compared to normal networks. Analyzing differentially connected genes can reveal new candidate cancer genes not identified by differential expression analysis alone. Integrating differential expression and differential connectivity data improves pathway enrichment analysis and provides insights into novel cancer-related pathways.
Integrative bioinformatics analysis of Parkinson's disease related omics data
Presentation on statistical meta analysis of omics data from Parkinson's disease case-control studies. The results are used for a comparative analysis against aging-related omics alterations in the brain and a prioritization of new candidate disease genes using the phenologs approach.
Analysis of gene expression microarray data of patients with Spinal Muscular ...
This document compares two approaches for identifying potential gene expression regulators from experimental data: Sub-Network Enrichment Analysis (SNEA) in Pathway Studio and Causal Reasoning in Ingenuity Pathway Analysis (IPA). It analyzes a publicly available dataset on Spinal Muscular Atrophy. SNEA identified biological functions and expression regulators more specifically related to neurogenesis, the key process affected in SMA. It produced results with greater relevance to the disease compared to IPA. Mapping genes to a motor neuron differentiation model also showed SNEA results were more consistent with SMN1 knockout.
Introduction to data integration in bioinformatics
This document provides an introduction to data integration in bioinformatics. It discusses different types of biological data including gene expression, copy number variations, epigenetic data like DNA methylation, microRNA data, clinical data, and pathways. It also discusses challenges of data integration like data heterogeneity, incompleteness, and frequent changes. Finally, it provides examples of two case studies that integrate different types of biological and imaging data to study liver cancer and lung cancer.
Genomics and proteomics
This document discusses genomics, proteomics, bioinformatics, pharmacogenomics, and the human genome project. It provides information on how genetic polymorphisms can influence drug disposition by affecting metabolizing enzymes and transporters. The human genome project mapped the entire human genome sequence to further the goals of personalized medicine based on an individual's genetic profile. Single nucleotide polymorphisms are particularly important for understanding how individuals respond differently to drugs.
mbb355 final
The document describes a method for designing a drug to target mutations in the BRCA1 gene that increase cancer risk. It first uses prediction software to analyze the BRCA1 structure and identify conserved domains. It then aligns BRCA1 with 8 similar protein structures to find homologous regions. The longest consistently aligned section, which also corresponds to a predicted functional domain, is identified as the best candidate region for a drug to target in order to reduce cancer risk from BRCA1 mutations.
Research proposal sjtu
The proposed research aims to develop a computational approach to analyze associations between transcription factor genes and diseases like cancer. The approach will extract gene-disease relationships from literature based on supporting evidence between genes, diseases, and evidence. Relationships will be quantitatively evaluated to extract strongly supported gene-disease linkages and rank them. Existing methods are reviewed that use properties of representative disease genes to find similar candidate genes, but the proposed method will emphasize verifiable evidence for predicted associations and their strength. The goal is to predict gene-disease relationships based on relationships between other entities to help discover disease genes.
Architecture of the human regulatory network derived from encode data
Transcription factors bind in a combinatorial fashion to specify the on-and-off states of genes; the ensemble of
these binding events forms a regulatory network, constituting the wiring diagram for a cell. To examine the
principles of the human transcriptional regulatory network, we determined the genomic binding information of
119 transcription-related factors in over 450 distinct experiments. We found the combinatorial, co-association of
transcription factors to be highly context specific: distinct combinations of factors bind at specific genomic locations.
In particular, there are significant differences in the binding proximal and distal to genes. We organized all the
transcription factor binding into a hierarchy and integrated it with other genomic information (for example,
microRNA regulation), forming a dense meta-network. Factors at different levels have different properties; for
instance, top-level transcription factors more strongly influence expression and middle-level ones co-regulate
targets to mitigate information-flow bottlenecks. Moreover, these co-regulations give rise to many enriched
network motifs (for example, noise-buffering feed-forward loops). Finally, more connected network components
are under stronger selection and exhibit a greater degree of allele-specific activity (that is, differential binding to the
two parental alleles). The regulatory information obtained in this study will be crucial for interpreting personal genome
sequences and understanding basic principles of human biology and disease.
Preproposal Talk
The document discusses challenges in querying over the Life Sciences Linked Open Data (LSLOD) cloud. It notes that the LSLOD cloud consists of many isolated and heterogeneous data sources, making queries difficult. Specifically, it outlines issues like inconsistent attribute values, incomplete entities and relations, inconsistent URIs, and inconsistent graph patterns between sources. The document proposes addressing this by mapping source schemas and graph patterns to a common model/ontology. This would allow rewriting queries to execute over multiple sources by translating between their representations and the common model.
TINAGL1 and B3GALNT1 are potential therapy target genes to suppress metastasi...
Presented at SIGBIO38, Okinawa, Japan, 26 June 2014
Full paper
http://www.ncbi.nlm.nih.gov/pubmed/25521548
IRJET- Disease Identification using Proteins Values and Regulatory Modules
1) The document proposes developing a common knowledge base for genomic and proteomic analysis to identify genetic disorders using regulatory modules.
2) It involves using collaborative filtering and depth first search to cluster gene ontology terms and regulatory modules for each gene expression.
3) Finally, a Bayesian rose tree is used to represent the taxonomy for a particular gene ID and identify associated diseases.
A Method to facilitate cancer detection and type classification from gene exp...
A Method to facilitate cancer detection and type classification from gene expression using a deep auto-encoder and neural network.
Genomics and Proteomics - Impact on Drug Discovery
A lecture on the impact of genomics and proteomics developments on drug discovery given in 2011 as part of a UCSD course SPPS273
Unravelling the molecular linkage of co morbid
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Unravelling the molecular linkage of co morbid diseases
Abstract ABSTRACT : The incidence of Diabetes Mellitus (DM), Hypertension (HTN) and Coronary artery disease (CAD) in the country has increased alarmingly. Since decades DM and HTN have been proved to be independent risk factors for CAD. Gene and its regulatory action through a protein are vital for the normal metabolism. Any abnormality in regulation would lead to a disease. Our study used the principles of network biology to understand the comorbidity of diseases at the molecular level. We have collected disease genes of DM, HTN and CAD from various public databases and extracted genes common to all the three diseases. We constructed a biological network by considering the protein interaction data obtained from Human Protein Reference Database (HPRD).The network was validated using power law distribution and the genes were ranked using Centiscape. Finally we identified the crucial genes with literature validation which could play a major role in causing disease co-morbidity. Keywords –Biological Network, Coronary Artery Disease, Diabetes Mellitus, Hypertension and Systems Biology
Topological analysis of coexpression networks in neoplastic tissues (BITS2012...
This document analyzes gene co-expression networks in normal and cancer tissues to identify genes that are differentially connected between the two conditions. It introduces a method to characterize disease genes based on statistically significant differences in a gene's degree (number of connections) between normal and cancer networks. Analysis of three cancer datasets finds subsets of differentially connected genes that are distinct from differentially expressed genes and are enriched for genes known to be related to the cancers. Differentially connected genes also alter network properties like average path length more than other gene sets when removed.
NGS Management And Analysis: From Sample To Molecular And Network Biology.
The Genomic Unit of the Center for Genomic Science of IIT@SEMM processes thousands of samples on Next Generation Sequencing platforms. We will briefly present how we manage the experimental flow and data with our dedicated LIMS and facilitate primary and secondary analyses with HTS-flow, a workflow management system that has been standardized and made easily accessible to both dry and wet lab scientists. Finally, we will show how we are extending genome visualization tools to enable the integration of NGS data with molecular, network and structural biology.
Presented at the "Giornata Milanese di NGS", 2016 Apr 8, University of Milan Bicocca
Network motifs in integrated cellular networks of transcription–regulation an...
This document analyzes network motifs in integrated cellular networks of transcription regulation and protein-protein interaction in Saccharomyces cerevisiae. The authors developed algorithms to detect composite network motifs involving both transcription factor regulation of genes and protein interactions. They identified a two-protein mixed feedback loop motif and five types of three-protein motifs exhibiting coregulation and complex formation as significant network motifs. Virtually all four-protein motifs consisted of combinations of these smaller motifs, suggesting they form basic building blocks of the cellular network.
Drug Discovery: Proteomics, Genomics
The document discusses drug discovery approaches using omics data and computational methods. It provides two examples: 1) Repositioning the Parkinson's disease drugs entacapone and tolcapone to treat tuberculosis by identifying a similar binding site on the M. tuberculosis enzyme InhA. 2) Explaining the side effects of the drug torcetrapib in a clinical trial for heart disease by identifying off-target binding to nuclear receptors like PPAR through structural analysis and docking simulations. The document advocates a network pharmacology approach considering multiple drug targets.
2011-NAR
This document discusses identifying transcriptional start sites (TSSs) of human microRNAs (miRNAs) based on high-throughput sequencing data. It integrates cap analysis of gene expression (CAGE) tags, TSS Seq libraries, and H3K4me3 chromatin signature data to identify 847 human miRNA TSSs, including 70 TSSs of miRNA clusters. A machine learning-based Support Vector Machine (SVM) model is developed to systematically select representative TSSs for each miRNA. The TSS of the important human miRNA hsa-miR-122 is experimentally validated to demonstrate the effectiveness of the proposed resource for identifying human miRNA TSSs.
A Classification of Cancer Diagnostics based on Microarray Gene Expression Pr...
inAbstract— Pattern Recognition (PR) plays an important role in field of Bioinformatics. PR is concerned with processing raw measurement data by a computer to arrive at a prediction that can be used to formulate a decision to be taken. The important problem in which pattern recognition are applied have common that they are too complex to model explicitly. Diverse methods of this PR are used to analyze, segment and manage the high dimensional microarray gene data for classification. PR is concerned with the development of systems that learn to solve a given problem using a set of instances, each instances represented by a number of features. The microarray expression technologies are possible to monitor the expression levels of thousands of genes simultaneously. The microarrays generated large amount of data has stimulate the development of various computational methods to different biological processes by gene expression profiling. Microarray Gene Expression Profiling (MGEP) is important in Bioinformatics, it yield various high dimensional data used in various clinical applications like cancer diagnostics and drug designing. In this work a new schema has developed for classification of unknown malignant tumors into known class. According to this work an new classification scheme includes the transformation of very high dimensional microarray data into mahalanobis space before classification. The eligibility of the proposed classification scheme has proved to 10 commonly available cancer gene datasets, this contains both the binary and multiclass data sets. To improve the performance of the classification gene selection method is applied to the datasets as a preprocessing and data extraction step.
Artículo review enfermedades neurodegenerativas 2013
This document reviews pathways implicated in neurodegeneration based on genome-wide association studies of Alzheimer's disease and Parkinson's disease. It discusses several intracellular pathways including apoptosis, autophagy, and mitochondrial dysfunction. Genes involved in these pathways that are associated with Alzheimer's and Parkinson's diseases are highlighted. The document also presents a conceptual model grouping these pathways into broader categories related to intracellular mechanisms, local tissue environment influences, systemic influences, and neurodevelopment and aging.
Medicine of the Future—The Transformation from Reactive to Proactive (P4) Med...
Medicine of the Future—The Transformation from Reactive to Proactive (P4) Medicine as presented at the Ohio State University Medical Center Personalized Health Care National Conference.
Leroy Hood, MD, PhD, is the president and founder of the Institute of Systems Biology. Dr. Hood is a member of the National Academy of Sciences, the American Philosophical Society, the American Academy of Arts and Sciences, the Institute of Medicine and the National Academy of Engineering. His professional career began at Caltech where he and his colleagues pioneered four instruments — the DNA gene sequencer and synthesizer and the protein synthesizer and sequencer — which comprise the technological foundation for contemporary molecular biology. In particular, the DNA sequencer played a crucial role in contributing to the successful mapping of the human genome during the 1990s.
http://www.systemsbiology.org/Scientists_and_Research
More Related Content
What's hot
Integrative bioinformatics analysis of Parkinson's disease related omics data
Presentation on statistical meta analysis of omics data from Parkinson's disease case-control studies. The results are used for a comparative analysis against aging-related omics alterations in the brain and a prioritization of new candidate disease genes using the phenologs approach.
Analysis of gene expression microarray data of patients with Spinal Muscular ...
This document compares two approaches for identifying potential gene expression regulators from experimental data: Sub-Network Enrichment Analysis (SNEA) in Pathway Studio and Causal Reasoning in Ingenuity Pathway Analysis (IPA). It analyzes a publicly available dataset on Spinal Muscular Atrophy. SNEA identified biological functions and expression regulators more specifically related to neurogenesis, the key process affected in SMA. It produced results with greater relevance to the disease compared to IPA. Mapping genes to a motor neuron differentiation model also showed SNEA results were more consistent with SMN1 knockout.
Introduction to data integration in bioinformatics
This document provides an introduction to data integration in bioinformatics. It discusses different types of biological data including gene expression, copy number variations, epigenetic data like DNA methylation, microRNA data, clinical data, and pathways. It also discusses challenges of data integration like data heterogeneity, incompleteness, and frequent changes. Finally, it provides examples of two case studies that integrate different types of biological and imaging data to study liver cancer and lung cancer.
Genomics and proteomics
This document discusses genomics, proteomics, bioinformatics, pharmacogenomics, and the human genome project. It provides information on how genetic polymorphisms can influence drug disposition by affecting metabolizing enzymes and transporters. The human genome project mapped the entire human genome sequence to further the goals of personalized medicine based on an individual's genetic profile. Single nucleotide polymorphisms are particularly important for understanding how individuals respond differently to drugs.
mbb355 final
The document describes a method for designing a drug to target mutations in the BRCA1 gene that increase cancer risk. It first uses prediction software to analyze the BRCA1 structure and identify conserved domains. It then aligns BRCA1 with 8 similar protein structures to find homologous regions. The longest consistently aligned section, which also corresponds to a predicted functional domain, is identified as the best candidate region for a drug to target in order to reduce cancer risk from BRCA1 mutations.
Research proposal sjtu
The proposed research aims to develop a computational approach to analyze associations between transcription factor genes and diseases like cancer. The approach will extract gene-disease relationships from literature based on supporting evidence between genes, diseases, and evidence. Relationships will be quantitatively evaluated to extract strongly supported gene-disease linkages and rank them. Existing methods are reviewed that use properties of representative disease genes to find similar candidate genes, but the proposed method will emphasize verifiable evidence for predicted associations and their strength. The goal is to predict gene-disease relationships based on relationships between other entities to help discover disease genes.
Architecture of the human regulatory network derived from encode data
Transcription factors bind in a combinatorial fashion to specify the on-and-off states of genes; the ensemble of
these binding events forms a regulatory network, constituting the wiring diagram for a cell. To examine the
principles of the human transcriptional regulatory network, we determined the genomic binding information of
119 transcription-related factors in over 450 distinct experiments. We found the combinatorial, co-association of
transcription factors to be highly context specific: distinct combinations of factors bind at specific genomic locations.
In particular, there are significant differences in the binding proximal and distal to genes. We organized all the
transcription factor binding into a hierarchy and integrated it with other genomic information (for example,
microRNA regulation), forming a dense meta-network. Factors at different levels have different properties; for
instance, top-level transcription factors more strongly influence expression and middle-level ones co-regulate
targets to mitigate information-flow bottlenecks. Moreover, these co-regulations give rise to many enriched
network motifs (for example, noise-buffering feed-forward loops). Finally, more connected network components
are under stronger selection and exhibit a greater degree of allele-specific activity (that is, differential binding to the
two parental alleles). The regulatory information obtained in this study will be crucial for interpreting personal genome
sequences and understanding basic principles of human biology and disease.
Preproposal Talk
The document discusses challenges in querying over the Life Sciences Linked Open Data (LSLOD) cloud. It notes that the LSLOD cloud consists of many isolated and heterogeneous data sources, making queries difficult. Specifically, it outlines issues like inconsistent attribute values, incomplete entities and relations, inconsistent URIs, and inconsistent graph patterns between sources. The document proposes addressing this by mapping source schemas and graph patterns to a common model/ontology. This would allow rewriting queries to execute over multiple sources by translating between their representations and the common model.
TINAGL1 and B3GALNT1 are potential therapy target genes to suppress metastasi...
Presented at SIGBIO38, Okinawa, Japan, 26 June 2014
Full paper
http://www.ncbi.nlm.nih.gov/pubmed/25521548
IRJET- Disease Identification using Proteins Values and Regulatory Modules
1) The document proposes developing a common knowledge base for genomic and proteomic analysis to identify genetic disorders using regulatory modules.
2) It involves using collaborative filtering and depth first search to cluster gene ontology terms and regulatory modules for each gene expression.
3) Finally, a Bayesian rose tree is used to represent the taxonomy for a particular gene ID and identify associated diseases.
A Method to facilitate cancer detection and type classification from gene exp...
A Method to facilitate cancer detection and type classification from gene expression using a deep auto-encoder and neural network.
Genomics and Proteomics - Impact on Drug Discovery
A lecture on the impact of genomics and proteomics developments on drug discovery given in 2011 as part of a UCSD course SPPS273
Unravelling the molecular linkage of co morbid
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Unravelling the molecular linkage of co morbid diseases
Abstract ABSTRACT : The incidence of Diabetes Mellitus (DM), Hypertension (HTN) and Coronary artery disease (CAD) in the country has increased alarmingly. Since decades DM and HTN have been proved to be independent risk factors for CAD. Gene and its regulatory action through a protein are vital for the normal metabolism. Any abnormality in regulation would lead to a disease. Our study used the principles of network biology to understand the comorbidity of diseases at the molecular level. We have collected disease genes of DM, HTN and CAD from various public databases and extracted genes common to all the three diseases. We constructed a biological network by considering the protein interaction data obtained from Human Protein Reference Database (HPRD).The network was validated using power law distribution and the genes were ranked using Centiscape. Finally we identified the crucial genes with literature validation which could play a major role in causing disease co-morbidity. Keywords –Biological Network, Coronary Artery Disease, Diabetes Mellitus, Hypertension and Systems Biology
Topological analysis of coexpression networks in neoplastic tissues (BITS2012...
This document analyzes gene co-expression networks in normal and cancer tissues to identify genes that are differentially connected between the two conditions. It introduces a method to characterize disease genes based on statistically significant differences in a gene's degree (number of connections) between normal and cancer networks. Analysis of three cancer datasets finds subsets of differentially connected genes that are distinct from differentially expressed genes and are enriched for genes known to be related to the cancers. Differentially connected genes also alter network properties like average path length more than other gene sets when removed.
NGS Management And Analysis: From Sample To Molecular And Network Biology.
The Genomic Unit of the Center for Genomic Science of IIT@SEMM processes thousands of samples on Next Generation Sequencing platforms. We will briefly present how we manage the experimental flow and data with our dedicated LIMS and facilitate primary and secondary analyses with HTS-flow, a workflow management system that has been standardized and made easily accessible to both dry and wet lab scientists. Finally, we will show how we are extending genome visualization tools to enable the integration of NGS data with molecular, network and structural biology.
Presented at the "Giornata Milanese di NGS", 2016 Apr 8, University of Milan Bicocca
Network motifs in integrated cellular networks of transcription–regulation an...
This document analyzes network motifs in integrated cellular networks of transcription regulation and protein-protein interaction in Saccharomyces cerevisiae. The authors developed algorithms to detect composite network motifs involving both transcription factor regulation of genes and protein interactions. They identified a two-protein mixed feedback loop motif and five types of three-protein motifs exhibiting coregulation and complex formation as significant network motifs. Virtually all four-protein motifs consisted of combinations of these smaller motifs, suggesting they form basic building blocks of the cellular network.
Drug Discovery: Proteomics, Genomics
The document discusses drug discovery approaches using omics data and computational methods. It provides two examples: 1) Repositioning the Parkinson's disease drugs entacapone and tolcapone to treat tuberculosis by identifying a similar binding site on the M. tuberculosis enzyme InhA. 2) Explaining the side effects of the drug torcetrapib in a clinical trial for heart disease by identifying off-target binding to nuclear receptors like PPAR through structural analysis and docking simulations. The document advocates a network pharmacology approach considering multiple drug targets.
2011-NAR
This document discusses identifying transcriptional start sites (TSSs) of human microRNAs (miRNAs) based on high-throughput sequencing data. It integrates cap analysis of gene expression (CAGE) tags, TSS Seq libraries, and H3K4me3 chromatin signature data to identify 847 human miRNA TSSs, including 70 TSSs of miRNA clusters. A machine learning-based Support Vector Machine (SVM) model is developed to systematically select representative TSSs for each miRNA. The TSS of the important human miRNA hsa-miR-122 is experimentally validated to demonstrate the effectiveness of the proposed resource for identifying human miRNA TSSs.
A Classification of Cancer Diagnostics based on Microarray Gene Expression Pr...
inAbstract— Pattern Recognition (PR) plays an important role in field of Bioinformatics. PR is concerned with processing raw measurement data by a computer to arrive at a prediction that can be used to formulate a decision to be taken. The important problem in which pattern recognition are applied have common that they are too complex to model explicitly. Diverse methods of this PR are used to analyze, segment and manage the high dimensional microarray gene data for classification. PR is concerned with the development of systems that learn to solve a given problem using a set of instances, each instances represented by a number of features. The microarray expression technologies are possible to monitor the expression levels of thousands of genes simultaneously. The microarrays generated large amount of data has stimulate the development of various computational methods to different biological processes by gene expression profiling. Microarray Gene Expression Profiling (MGEP) is important in Bioinformatics, it yield various high dimensional data used in various clinical applications like cancer diagnostics and drug designing. In this work a new schema has developed for classification of unknown malignant tumors into known class. According to this work an new classification scheme includes the transformation of very high dimensional microarray data into mahalanobis space before classification. The eligibility of the proposed classification scheme has proved to 10 commonly available cancer gene datasets, this contains both the binary and multiclass data sets. To improve the performance of the classification gene selection method is applied to the datasets as a preprocessing and data extraction step.
What's hot (20)
Integrative bioinformatics analysis of Parkinson's disease related omics data
Integrative bioinformatics analysis of Parkinson's disease related omics data
Analysis of gene expression microarray data of patients with Spinal Muscular ...
Analysis of gene expression microarray data of patients with Spinal Muscular ...
Introduction to data integration in bioinformatics
Introduction to data integration in bioinformatics
Architecture of the human regulatory network derived from encode data
Architecture of the human regulatory network derived from encode data
TINAGL1 and B3GALNT1 are potential therapy target genes to suppress metastasi...
TINAGL1 and B3GALNT1 are potential therapy target genes to suppress metastasi...
IRJET- Disease Identification using Proteins Values and Regulatory Modules
IRJET- Disease Identification using Proteins Values and Regulatory Modules
A Method to facilitate cancer detection and type classification from gene exp...
A Method to facilitate cancer detection and type classification from gene exp...
Genomics and Proteomics - Impact on Drug Discovery
Genomics and Proteomics - Impact on Drug Discovery
Unravelling the molecular linkage of co morbid diseases
Unravelling the molecular linkage of co morbid diseases
Topological analysis of coexpression networks in neoplastic tissues (BITS2012...
Topological analysis of coexpression networks in neoplastic tissues (BITS2012...
NGS Management And Analysis: From Sample To Molecular And Network Biology.
NGS Management And Analysis: From Sample To Molecular And Network Biology.
Network motifs in integrated cellular networks of transcription–regulation an...
Network motifs in integrated cellular networks of transcription–regulation an...
A Classification of Cancer Diagnostics based on Microarray Gene Expression Pr...
A Classification of Cancer Diagnostics based on Microarray Gene Expression Pr...
Similar to A Network View on Parkinson’s Disease Elsevier webinar 15 jan 2015
Artículo review enfermedades neurodegenerativas 2013
This document reviews pathways implicated in neurodegeneration based on genome-wide association studies of Alzheimer's disease and Parkinson's disease. It discusses several intracellular pathways including apoptosis, autophagy, and mitochondrial dysfunction. Genes involved in these pathways that are associated with Alzheimer's and Parkinson's diseases are highlighted. The document also presents a conceptual model grouping these pathways into broader categories related to intracellular mechanisms, local tissue environment influences, systemic influences, and neurodevelopment and aging.
Medicine of the Future—The Transformation from Reactive to Proactive (P4) Med...
Medicine of the Future—The Transformation from Reactive to Proactive (P4) Medicine as presented at the Ohio State University Medical Center Personalized Health Care National Conference.
Leroy Hood, MD, PhD, is the president and founder of the Institute of Systems Biology. Dr. Hood is a member of the National Academy of Sciences, the American Philosophical Society, the American Academy of Arts and Sciences, the Institute of Medicine and the National Academy of Engineering. His professional career began at Caltech where he and his colleagues pioneered four instruments — the DNA gene sequencer and synthesizer and the protein synthesizer and sequencer — which comprise the technological foundation for contemporary molecular biology. In particular, the DNA sequencer played a crucial role in contributing to the successful mapping of the human genome during the 1990s.
http://www.systemsbiology.org/Scientists_and_Research
FunGen JC Presentation - Mostafavi et al. (2019)
The document summarizes a study that used RNA sequencing data from over 500 brain samples to construct gene coexpression networks and identify modules of genes associated with Alzheimer's disease traits like cognitive decline and amyloid pathology. They prioritized one module (m109) associated with cognitive decline and validated two genes (INPPL1 and PLXNB1) within that module that reduced amyloid levels in vitro. However, these genes did not fully account for the effects of the entire module, suggesting more work is needed to identify additional driver genes related to cognitive decline in Alzheimer's disease.
Austin Neurology & Neurosciences
Austin Neurology & Neurosciences is an open access, peer reviewed, scholarly journal dedicated to publish articles covering all areas of Neurology & Neurological Sciences.
The journal aims to promote research communications and provide a forum for doctors, researchers, physicians and healthcare professionals to find most recent advances in all areas of Neurology & Neurological Sciences. Austin Neurology & Neurosciences accepts original research articles, reviews, mini reviews, case reports and rapid communication covering all aspects of neurology & neurosciences.
Austin Neurology & Neurosciences strongly supports the scientific up gradation and fortification in related scientific research community by enhancing access to peer reviewed scientific literary works. Austin Publishing Group also brings universally peer reviewed journals under one roof thereby promoting knowledge sharing, mutual promotion of multidisciplinary science.
How to analyse large data sets
1. Statistical analysis of big data sets from microarrays and RNA-seq is used to identify differentially expressed genes. Heat maps and volcano plots are commonly used to visualize the data.
2. Gene ontology, gene set enrichment analysis, and transcription factor analysis are used to analyze lists of genes and identify biological processes, pathways, and regulatory relationships.
3. Networks can be constructed by integrating gene lists with protein-protein and gene regulatory interaction databases to build signaling, regulatory, and interaction networks for further analysis.
Introduction to Network Medicine
This document provides an introduction to network medicine and discusses its application to chronic obstructive pulmonary disease (COPD). It defines network medicine as studying cellular, disease, and social networks to quantify factors contributing to individual diseases. For COPD, network approaches are being used to build disease networks, define molecular pathways, identify optimal disease phenotypes, develop new classifications, and integrate multi-omics data. Genome-wide association studies have identified risk loci for COPD, which are now being functionally validated in cellular and animal models. Network medicine aims to take a holistic rather than reductionist approach to complex diseases like COPD.
BRN Seminar 12/06/14 Introduction to Network Medicine
This document provides an introduction to network medicine and discusses its application to chronic obstructive pulmonary disease (COPD). It defines network medicine as studying cellular, disease, and social networks to quantify factors contributing to individual diseases. For COPD, network approaches are being used to build disease networks, define molecular pathways, identify optimal disease phenotypes, develop new classifications, and integrate multi-omics data. Genome-wide association studies have identified risk loci for COPD, which are now being functionally validated in cellular and animal models. Network medicine aims to take a holistic rather than reductionist approach to complex diseases like COPD.
GENE-GENE INTERACTION ANALYSIS IN ALZHEIMER
ABSTRACT
Genome-wide transcription profiling is a powerful technique in studying disease susceptible footprints. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. Past researches prove that neuro diseases damage the brain network interaction, protein- protein interaction and gene-gene interaction. A number of neurological research paper also analyze the relationship among damaged part. Analysis of gene-gene interaction network drawn by using state-of-the-art gene database of Alzheimer’s patient can conclude a lot of information. In this paper we used gene dataset affected with Alzheimer’s disease and normal patient’s dataset from NCBI databank. After proper processing the .CEL affymetrix data using RMA, we use the processed data to find gene interaction outputs. Then we filter the output files using probe set filtering attributes p-value and fold count and draw a gene-gene interaction network. Then we analyze the interaction network using GeneMania software.
GENE-GENE INTERACTION ANALYSIS IN ALZHEIMER
Genome-wide transcription profiling is a powerful technique in studying disease susceptible footprints. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. Past researches prove that neuro diseases damage the brain network interaction, protein- protein interaction and gene-gene interaction. A number of neurological research paper also analyze the relationship among damaged part. Analysis of gene-gene interaction network drawn by using state-of-the-art gene database of Alzheimer’s patient can conclude a lot of information. In this paper we used gene dataset affected with Alzheimer’s disease and normal patient’s dataset from NCBI databank. After proper processing the .CEL affymetrix data using RMA, we use the processed data to find gene interaction outputs. Then we filter the output files using probe set filtering attributes p-value and fold count and draw a gene-gene interaction network. Then we analyze the interaction network using GeneMania software.
CSHL_poster_Brain_July_2016_final_version
This document describes a portal that integrates publicly available connectomic and gene expression data with RNA-seq data to help identify genes of interest in neurodegenerative diseases like Parkinson's. Specifically, it uses retrograde tracing analysis (Retro-TRAP) on dopamine neurons and linear regression of RNA-seq data comparing disease vs normal states. Key genes identified are validated using the Allen Brain Atlas gene expression data stored locally. This combined approach helps pinpoint genes selectively expressed in neural circuits involved in diseases like Parkinson's that show selective neurodegeneration. The portal was created using Python/Django and allows users to upload and analyze multi-omic data interactively to advance understanding of neurodegenerative disease etiology.
Algorithmically Optimized Gene Selection for Targeted Clinical Sequencing Panels
Algorithmically Optimized Gene Selection for Targeted Clinical Sequencing PanelsThermo Fisher Scientific
Selection of genes to include in genomic studies of disease
remains a difficult task. Current methods rely on expert opinion
or manual search engine use. With these methods, the
process and result are neither repeatable nor scalable. To
remedy this situation, we created the Informative Genetic
Content (IGC) system, which enables the algorithmic selection
of genes for inclusion in such studies, given one or more
diseases to target.
The IGC system stands on three components: a database
associating diseases with genes and other diseases, an
algorithm to rank the genes under consideration for inclusion in
a panel, and a module that clusters genes by families of
diseases. The first component, the database, maps diseases
to associated genes and scores each of these mappings
according to the strength of the relationship. The database also
maps diseases to other diseases, such that groups of diseases
or hierarchical relationships between diseases can be
identified. The second component enables the ranking of
candidate genes when multiple diseases are of interest. The
algorithm accounts for the common situation where two or
more diseases are associated with the same gene with varying
strengths of association, weighting and combining the scores
across the diseases associated with each gene. The final
component, the gene clustering module, groups genes by
pathogenic pathways, should the user want to consider
targeting a broader family of diseases affected by a closely
related set of genes.
We validated the IGC system through comparisons of our
automated gene selections with expertly curated gene panel
designs. We found a high degree of overlap between the IGC’s
gene selection and the gene lists chosen by experts,
supporting the viability of our system.
Together with the scalability and repeatability enabled by its
automation, the IGC system greatly improves the gene panel
selection process and therefore advances targeted genomic
studies.Bartus, et al (review gene rx and pd) mol therapy 2014
Bartus, et al (review gene rx and pd) mol therapy 2014Raymond T. Bartus, PhD (RTBioconsultants, Inc)
This document discusses 9 gene therapy clinical trials conducted over the past decade to treat Parkinson's disease. While the trials demonstrated that gene therapy can safely generate biologically active proteins in the brain without risk of harm, none produced sufficiently robust clinical efficacy to enable further trials or regulatory approval. The document analyzes the reasons for the lack of efficacy, including limitations in assessing dosing levels and transgene expression in the brain. It reviews the 5 different gene therapy approaches tested so far targeting various parts of the basal ganglia, and discusses lessons learned regarding trial design, patient selection, and the need for improved animal models.Parkinson’s diagnosis hybrid system based on deep learning classification wit...
Brain degeneration involves several neurological troubles such as Parkinson’s disease (PD). Since this neurodegenerative disorder has no known cure, early detection has a paramount role in improving the patient’s life. Research has shown that voice disorder is one of the first symptoms detected. The application of deep learning techniques to data extracted from voice allows the production of a diagnostic support system for the Parkinson’s disease detection. In this work, we adopted the synthetic minority oversampling technique (SMOTE) technique to solve the imbalanced class problems. We performed feature selection, relying on the Chi-square feature technique to choose the most significant attributes. We opted for three deep learning classifiers, which are long-short term memory (LSTM), bidirectional LSTM (Bi-LSTM), and deep-LSTM (D-LSTM). After tuning the parameters by selecting different options, the experiment results show that the D-LSTM technique outperformed the LSTM and Bi-LSTM ones. It yielded the best score for both the imbalanced original dataset and for the balanced dataset with accuracy scores of 94.87% and 97.44%, respectively.
MOJPB-03-00085
1) The document discusses emerging proteomics and genomics biomarkers for diagnosing Alzheimer's disease (AD), noting that current biomarkers like cerebrospinal fluid levels have limited accuracy.
2) It suggests that multi-biomarker approaches combining proteins associated with AD and vascular diseases may improve diagnostic accuracy.
3) MicroRNAs found in biofluids like cerebrospinal fluid and blood show potential as AD biomarkers, and combining multiple biomarkers into diagnostic profiles could help predict which individuals will progress from mild cognitive impairment to AD dementia.
Analisis de la expresion de genes en la depresion
DNA microarray technologies have advanced in the past 15 years and allow measurement of thousands of gene expressions simultaneously. This study analyzed gene expression profiles in the amygdala and anterior cingulate of depressed patients compared to controls. The authors found correlated changes in these brain regions in both human depressed patients and an animal model of depression. Specifically, they identified decreased expression of oligodendrocyte genes in the amygdala and changes to glial-neuronal communication pathways. By analyzing gene expression across brain regions and species, and confirming results with additional techniques, this study establishes new targets for investigating molecular changes underlying depression.
UCSC Qualifying Exam Proposal 2012
This document discusses finding biomarkers for Parkinson's disease (PD) using feature selection methods on gene expression data. The author aims to identify potential biomarkers for early-stage idiopathic PD (IPD) using data from induced pluripotent stem cell (iPSC)-derived dopaminergic neurons from an IPD patient. The author will apply fold change, moderated t-statistic, information-theoretic feature selection, and recursive feature elimination with ridge regression (RFE-RR) to identify biomarker gene subsets. The author will also compare biomarkers from early-stage iPSC data to late-stage post-mortem data to investigate differences between early and late PD.
0a85e52d7e91569ab8000000(1)
This study examined four coding polymorphisms in the PARKIN gene (Ser167Asn, Val380Leu, Arg366Trp, and Asp394Asn) in a population from northeastern Mexico to determine their association with Parkinson's disease (PD). The study compared 117 unrelated PD patients to 122 unrelated healthy controls. Genotyping was performed using PCR-RFLP. No association was found between the Ser167Asn and Val380Leu polymorphisms and PD. For controls, these two polymorphisms showed Hardy-Weinberg disequilibrium, which could be due to a competing risk of death from a mutant gene. The Trp366 and Asn394 genotypes were not present. In conclusion, the PARK
Introducción a la bioinformatica
The document introduces bioinformatics and discusses its goals and applications. Bioinformatics involves using computational tools and databases to analyze and understand biological data like DNA, RNA, and proteins. It has two main subfields - developing computational tools and databases, and applying these tools to generate biological knowledge and insights into living systems. Bioinformatics aims to better understand cells at the molecular level and how they function. It has applications in areas like drug design, forensics, agriculture, and medicine.
Human genome
The human genome contains around 3 billion base pairs and 20,000-25,000 genes. Genes code for proteins and can vary in length from 1,000 to over 1.5 million base pairs. While genes make up about 1-1.4% of the genome, the remaining non-coding regions also play important regulatory roles. Genetic variations, from single mutations to complex interactions between multiple genes and the environment, underlie many diseases. New sequencing technologies are helping researchers better understand these relationships and develop personalized prevention and treatment approaches.
Gasparini_2014_02Thesis
This document summarizes a PhD thesis that investigated genetic variants within candidate genes that may increase susceptibility to migraine. The thesis examined variants in genes related to glutamate signaling and RNA editing, including GRIA2, GRIA4, GLUD1, GLUD2, ADARB1 and ADARB2 in an Australian case-control population. No significant associations were found for the glutamate-related genes. Two SNPs in ADARB2 showed nominal associations with migraine risk. The thesis also validated mutations identified in exome sequencing of three migraine-linked families, warranting further study of ADARB2 and these mutations to understand their influence on migraine.
Similar to A Network View on Parkinson’s Disease Elsevier webinar 15 jan 2015 (20)
Artículo review enfermedades neurodegenerativas 2013
Artículo review enfermedades neurodegenerativas 2013
Medicine of the Future—The Transformation from Reactive to Proactive (P4) Med...
Medicine of the Future—The Transformation from Reactive to Proactive (P4) Med...
BRN Seminar 12/06/14 Introduction to Network Medicine
BRN Seminar 12/06/14 Introduction to Network Medicine
Algorithmically Optimized Gene Selection for Targeted Clinical Sequencing Panels
Algorithmically Optimized Gene Selection for Targeted Clinical Sequencing Panels
Bartus, et al (review gene rx and pd) mol therapy 2014
Bartus, et al (review gene rx and pd) mol therapy 2014
Parkinson’s diagnosis hybrid system based on deep learning classification wit...
Parkinson’s diagnosis hybrid system based on deep learning classification wit...
More from Ann-Marie Roche
How predictive models help Medicinal Chemists design better drugs_webinar
All scientific disciplines, including medicinal chemistry, are experiencing a revolution in unprecedented rates of data being generated and the subsequent analysis and exploitation of this data is increasingly fundamental to innovation. Using data to design better compounds is a challenge for Medicinal and Computational chemists.
The design of small-molecule drug candidates, encompassing characteristics such as potency, selectivity and ADMET (absorption, distribution, metabolism, excretion and toxicity) is a key factor in the success of clinical trials and computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based.
In this webinar our expert Dr. Olivier Barberan will discuss ligand-based methods and he will cover the following:
How to use only ligand information to predict activity depending on its similarity/dissimilarity to previously known active ligands.
- Discuss ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships and important tools such as target/ligand databases necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign.
Webinar: New RMC - Your lead_optimization Solution June082017
The document discusses Reaxys Medicinal Chemistry and how it supports hit-to-lead and lead optimization processes. It provides high quality data on topics like efficacy, ADMET properties, and animal models to help computational and medicinal chemists. The pX concept normalizes bioactivity measurements like IC50, Ki, and % inhibition into a single comparable metric, making it possible to compare compound affinity regardless of the metric reported. This allows researchers to more easily search for and analyze active compounds.
Oil&Gas Thought Leader Webinar - New Plays for Old Ideas - Dr.Gabor Tari
In our April 2017 webinar, three industry experts shared their research and demonstrated the importance of focusing on fundamental geologic and geophysical research approaches that integrate variety of data, information and concepts from disparate sources and related disciplines.
This back-to-fundamentals research can both inspire and accelerate exploration teams’ thinking about petroleum systems and lead to a path to success.
Dr Gabor Tari is currently the Group Chief Geologist at OMV. He has over 20 years’ experience working in upstream oil & gas and has worked for Amoco, BP, and Vanco, before joining OMV in 2007. Gabor has worked on exploration projects in basins around the globe, including Romania, Angola, North Africa, and the Middle East. He has authored over 50 scientific publications, presented papers at dozens of conferences, and most recently co-authored the book Permo-Triassic Salt Provinces of Europe, North Africa and the Atlantic Margins, with Dr Joan Flinch (Repsol) and Juan Soto, Professor of Geodynamics in the Granada University and in the Instituto Andaluz de Ciencias de la Tierra, Spain, which is currently available from Elsevier for pre-order online.
Gabor discussed and shared some examples of how new plays can be built on a solid foundation of petroleum system development and research, and how new ideas can be garnered from building on published research of oil & gas companies, academia, service providers and consultants.
Oil&Gas Thought-Leader Webinar - New Plays for Old Ideas - Dr. Rob Forkner
In our April 2017 webinar, three industry experts shared their research and demonstrated the importance of focusing on fundamental geologic and geophysical research approaches that integrate variety of data, information and concepts from disparate sources and related disciplines. This back-to-fundamentals research can both inspire and accelerate exploration teams’ thinking about petroleum systems and lead to a path to success.
Dr Rob Forkner is a carbonate geologist at Statoil, working in the carbonate plays and reservoirs research group in Austin, Texas, focusing on carbonate play prediction in Atlantic margin systems. Prior to Statoil, Rob worked at Maersk and Shell in onshore and offshore in well planning, geosteering, high-resolution sequence stratigraphy and facies prediction, carbonate sedimentology in unconventional assets, evaporite classification and prediction, rock typing, and more recently, carbonate system suppression and recovery during Oceanic Anoxic Events.
Oil&Gas Thought-Leader Webinar - New Plays for Old Ideas - Dr. Sander Houben
Dr. Sander Houben presented on combining paleoceanographic and exploration tools to study Early Jurassic anoxic events. He discussed how carbon isotopes can be used as a stratigraphic tool to analyze perturbations to the carbon cycle during these events. Palynological analysis of indicators of photic zone anoxia and chemocline migration provided insight into changes in water column ecology. A case study of the Toarcian OAE and Posidonia Shale Formation showed how isotopic analyses revealed a major increase in export of hydrogen-rich organic matter due to intensified primary productivity by diazotrophs under low oxygen conditions. Paleoceanographic observations combined with an exploration geology perspective provided understanding of the formation of
Embase for pharmacovigilance: Search and validation March 22 2017
Scientific literature plays a critical role in Pharmacovigilance and Drug Safety workflows. Monitoring literature for mentions of adverse drug reactions (ADRs) is mandated by regulatory bodies, and marketing authorization holders (MAHs) that do not properly report ADRs can be subject to heavy fines. With an increasing volume of unstructured content to cover, along with rising labor costs, MAHs are looking for ways to make their literature monitoring more effective and efficient.
Abstract and indexing (A&I) databases play an important role in Literature Monitoring – due to the vast amount of scientific literature published daily – in order for MAH’s to locate specific articles or conference presentations that may be relevant for their products (for both benefit/risk analysis and ADR detection). Rather than reading all the literature, MAH’s create search strategies that identify the relevant records in A&I databases and execute the searches regularly. GVP module VI mandates that searches are done at least weekly, but many companies maintain a daily monitoring and review cycle.
In this webinar, Senior Product Development Manager Embase, Dr. Ivan Krstic discussed best practices for saving time, staying current, validating search strategies and mitigating risk in the face of these increasingly complex processes in literature monitoring
Literature Management for Pharmacovigilance: Outsource or in-house solution? ...
Pharmaceutical companies are required to screen scientific literature on a regular basis and this comes with many challenges, such as handling large amounts of data, building search strings and integrating EMA MLM results. Out-sourcing literature screening to service providers reduces the workload for the PV-team, but how does it impact the literature management process overall? Maybe it results in decreased oversight and additional activities like audits and reconciliation? And what about building the search strategy?
During this webinar our PV expert, Dr. Joyce De Langen spoke about the following:
• The importance of literature management in Pharmacovigilance and the challenges.
• An evaluation of the benefits and risks of outsourcing literature management versus alternative solutions.
About the speaker:
Joyce de Langen, Ph.D has more than 10 years of experience in the domain of pharmacovigilance and drug safety. Through her work in the pharmaceutical industry, academia and regulatory authorities, Joyce has developed a broad perspective and knowledge in pharmacovigilance and drug safety.
Finding the right medical device information in embase 11 2016
The document discusses guidelines for systematic reviews of biomedical literature in Clinical Evaluation Reports (CERs) for medical devices, highlighting how Embase addresses the requirements through its comprehensive indexing of devices, manufacturers, and adverse effects, as well as features for building sensitive searches. It also provides examples of searches in Embase to find information on device clinical performance, comparisons, and safety for a case study on an everolimus eluting coronary stent.
Medical device reporting 27 sep2016
The document discusses medical device adverse event reporting requirements, including definitions of reportable events and timelines for submitting reports to regulatory agencies. It provides an overview of the classification system for medical devices and regulations around reporting malfunctions, deaths and serious injuries caused by devices. Reporting requirements and challenges involving software as a medical device are also reviewed.
Eac webinar 09.21.2016
The All-New 2016 Engineering Academic Challenge - developed by students for students
The Engineering Academic Challenge (formerly as the Knovel Academic Challenge) is an immersive, 5-week interactive problem-set competition, featuring weekly thematic engineering challenges built around five transdisciplinary themes inspired by the National Academy of Engineering Grand Challenges.
Literature monitoring for pv what are we doing at galderma elsevier webinar
The document discusses literature monitoring for pharmacovigilance. It describes weekly monitoring of individual case safety reports and periodic monitoring through development safety update reports and periodic benefit-risk evaluation reports. Key databases for literature searches are Medline and Embase. While Embase has more extensive drug coverage, searches on Medline via PubMed are more reliable due to the potential for loss of MeSH subheadings when mapping to Emtree and the risk of false negatives and positives when searching Embase alone. Literature searches support signal detection and periodic evaluation of a product's safety profile.
Drug analytics based on triple linking v1.0
This document discusses how drug analytics based on manually extracted semantic relationships in Embase can be useful for drug development, repurposing, and safety. It describes how relationships between drugs, diseases, and adverse reactions that are manually indexed can provide valuable information for drug repurposing, development, and safety. Specific examples are provided to show how the semantic relationships can guide drug repositioning strategies, investigate new combination drugs, identify drug-drug interactions, collect drug comparison data, and help improve risk management.
Knovel lss webinar
This document discusses Lean Six Sigma and resources available through Knovel to support Lean Six Sigma implementation. It provides an overview of the Lean Six Sigma implementation process including strategic leadership and vision, deployment planning, and execution and results. It describes Knovel's Lean Six Sigma resources such as handbooks, case studies, templates, and guides covering tools like DMAIC, DOE, SPC etc. that can help with the different belts and project phases from Define to Control. Other resources discussed include those for Design for Six Sigma and practical applications/case studies.
Reaxys rmc unified platform_ webinar_
Reaxys provides a unified information portal that integrates data from multiple chemistry sources through a single interface. It links chemistry data, structures, citations, and full-text articles. Reaxys also integrates in-house data from sources like electronic lab notebooks through its API and can be used for activities like compound screening, literature searching, and patent analysis to support drug discovery.
Pathway studiosymposium lorenzi
Phil Lorenzi discusses pathway analysis approaches and their uses in biomedical research and drug development. He compares strategies for analyzing the autophagy and apoptosis pathways, finding that integrating multiple methods provides the most comprehensive understanding. Lorenzi also provides examples of how pathway analysis could have predicted problems with COX-2 inhibitors and helped explain past failures of AKT inhibitors. He concludes that pathway analysis is consistent with approvals of EGFR, MEK, RANKL and PARP inhibitors and may support development of GLS inhibitors.
Searching literature databases for post authorisation safety studies (pass)
This document discusses using literature databases like Embase to conduct post-authorization safety studies (PASS) through systematic literature reviews and meta-analyses. It provides an example PASS on the drug brentuximab vedotin that identified adverse events like peripheral neuropathy and infections. The document reviews how to structure a literature search using the PICO framework and Embase's in-depth indexing of concepts, relationships, and causality to comprehensively identify safety outcomes reported for a drug.
Julie glanville embase sunrise seminar may 2016
Simple text mining tools can help Embase users in several ways:
- Frequency analysis of terms in records can identify useful search terms and concepts to explore. Tools like EndNote and Voyant allow viewing frequencies of words in titles, abstracts, and subject headings.
- Phrase analysis identifies common word combinations or concepts in the text, beyond single words. Voyant and TERMINE are useful for this.
- Word collocation analysis shows which words frequently occur near each other, suggesting relationships between ideas. The Voyant collocates tool supports this.
- Cluster and network visualizations identify major themes or concepts within a set of records. VOSviewer creates visual maps of related terms.
Exploring records
Ian crowlesmith embase retrospective mla 2016
Embase began in 1946 as Excerpta Medica, founded to provide medical abstracts. It was acquired by Elsevier in 1971 and became available online in 1978. Key developments included introducing a controlled vocabulary called Emtree in 1987 and adding item types and check tags for evidence-based medicine in 1990. Currently, Embase indexes articles in great depth using natural language and extensively covers drugs and devices. The taxonomy Emtree is regularly updated to reflect new terms.
Ivan krstic embase update mla 2016
The document provides an update on new features and enhancements to Embase.com. Key points include:
- The addition of a new PICO search page that allows users to build clinical searches by splitting questions into Patient, Intervention, Comparison, and Outcome elements.
- Other enhancements include improved search tips, the ability to add synonyms and view all abstracts, as well as analytics capabilities for drug safety and repurposing based on triple indexing of content.
- Future plans include improvements to content, taxonomy, and indexing as well as a revamp of the search platform interface and functionality.
Kp bloch psm preparedness final rev
This document discusses upcoming changes to process safety management (PSM) regulations and standards. It notes several major industrial accidents in recent decades that prompted reforms. New PSM requirements in California will likely be adopted more widely and require more prescriptive tasks, reporting, and accountability. To ensure future PSM success, the document recommends: making no distinction between internal/external compliance; expanding the definition of mechanical integrity; understanding "double jeopardy"; not replacing investigations with management of change; knowing what the operations team is doing; and clarifying teamwork expectations regarding stop work authorizations.
More from Ann-Marie Roche (20)
How predictive models help Medicinal Chemists design better drugs_webinar
How predictive models help Medicinal Chemists design better drugs_webinar
Webinar: New RMC - Your lead_optimization Solution June082017
Webinar: New RMC - Your lead_optimization Solution June082017
Oil&Gas Thought Leader Webinar - New Plays for Old Ideas - Dr.Gabor Tari
Oil&Gas Thought Leader Webinar - New Plays for Old Ideas - Dr.Gabor Tari
Oil&Gas Thought-Leader Webinar - New Plays for Old Ideas - Dr. Rob Forkner
Oil&Gas Thought-Leader Webinar - New Plays for Old Ideas - Dr. Rob Forkner
Oil&Gas Thought-Leader Webinar - New Plays for Old Ideas - Dr. Sander Houben
Oil&Gas Thought-Leader Webinar - New Plays for Old Ideas - Dr. Sander Houben
Embase for pharmacovigilance: Search and validation March 22 2017
Embase for pharmacovigilance: Search and validation March 22 2017
Literature Management for Pharmacovigilance: Outsource or in-house solution? ...
Literature Management for Pharmacovigilance: Outsource or in-house solution? ...
Finding the right medical device information in embase 11 2016
Finding the right medical device information in embase 11 2016
Literature monitoring for pv what are we doing at galderma elsevier webinar
Literature monitoring for pv what are we doing at galderma elsevier webinar
Searching literature databases for post authorisation safety studies (pass)
Searching literature databases for post authorisation safety studies (pass)
Recently uploaded
Global Situational Awareness of A.I. and where its headed
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
End-to-end pipeline agility - Berlin Buzzwords 2024
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
Intelligence supported media monitoring in veterinary medicine
Media monitoring in veterinary medicien
一比一原版(Unimelb毕业证书)墨尔本大学毕业证如何办理
原版制作【微信:41543339】【(Unimelb毕业证书)墨尔本大学毕业证】【微信:41543339】《成绩单、外壳、雅思、offer、留信学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同进口机器一比一制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
DSSML24_tspann_CodelessGenerativeAIPipelines
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
The Ipsos - AI - Monitor 2024 Report.pdf
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
毕业原版【微信:176555708】【(GWU,GW毕业证书)乔治·华盛顿大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Analysis insight about a Flyball dog competition team's performance
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Learn SQL from basic queries to Advance queries
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
在线办理(英国UCA毕业证书)创意艺术大学毕业证在读证明一模一样
学校原件一模一样【微信：741003700 】《(英国UCA毕业证书)创意艺术大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Experts live - Improving user adoption with AI
Bekijk de slides van onze sessie Enhancing Modern Workplace Efficiency op Experts Live 2024.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Open Source Contributions to Postgres: The Basics POSETTE 2024
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
原版一模一样【微信：741003700 】【(uts毕业证书)悉尼科技大学毕业证学历证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
University of New South Wales degree offer diploma Transcript
澳洲UNSW毕业证书制作新南威尔士大学假文凭定制Q微168899991做UNSW留信网教留服认证海牙认证改UNSW成绩单GPA做UNSW假学位证假文凭高仿毕业证申请新南威尔士大学University of New South Wales degree offer diploma Transcript
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"
Embark on a captivating financial journey with 'Financial Odyssey,' our hackathon project. Delve deep into the past performance of two companies as we employ an array of financial statement analysis techniques. From ratio analysis to trend analysis, uncover insights crucial for informed decision-making in the dynamic world of finance."
Recently uploaded (20)
Global Situational Awareness of A.I. and where its headed
Global Situational Awareness of A.I. and where its headed
A presentation that explain the Power BI Licensing
A presentation that explain the Power BI Licensing
End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024
Intelligence supported media monitoring in veterinary medicine
Intelligence supported media monitoring in veterinary medicine
Challenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more important
Analysis insight about a Flyball dog competition team's performance
Analysis insight about a Flyball dog competition team's performance
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...
Open Source Contributions to Postgres: The Basics POSETTE 2024
Open Source Contributions to Postgres: The Basics POSETTE 2024
University of New South Wales degree offer diploma Transcript
University of New South Wales degree offer diploma Transcript
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"
A Network View on Parkinson’s Disease Elsevier webinar 15 jan 2015
- 2. A Network View on Parkinson’s Disease Danail Bonchev Center for the Study of Biological Complexity Virginia Commonwealth University Elsevier, January 15, 2015 Based on: S. Chandrasekaran , D. Bonchev, Comput. Struct. Biotechn. J. 7(8), 2013, e201304004 (free downloads from researchgate.net/profile/Danail_Bonchev) .
- 3. Introduction The Neurodegenerative Diseases – The Known and the Unknown The Network Approach: What could it help? Understanding better the molecular mechanisms of these diseases will bring new effective drug candidates, as well as molecular markers for early warning for incoming problems. This report presents the results for a detailed network analysis of the Parkinson’s disease, as a part of a search for a common underlying mechanism of neurodegenerative diseases, including Alzheimer’s and Huntington’s diseases. The network analysis is performed using Pathway Studio software as a basic tool, supplemented for some specific aims by other software packages.
- 4. Figure 1. Biological processes and genes implicated in the Parkinson’s disease What was known
- 5. Nine genes have previously been associated with dominant or recessive form of Parkinson’s disease: 9 genes: ATPI3A2, DJ-1 GIGYF2, HTRA2, LRRK2, PARK (parkin), PINK1, SNCA , UCHL1 SNCA (α-synuclein or α-syn) is critical to the early stage pathogenesis. This list was updated after extensive online search, so as to create one of our “seed genes” list, a preliminary step in our approach. Compiling a list of genes implicated with Parkinson’s disease
- 6. Work Flow
- 7. A B Figure 3. Four-set Venn diagram of the overlap of significantly differentially expressed genes (SDEGs) in (a) GSE8397 HG-U133A (b) GSE8397 HG-U133B and (c) GSE20295 HG-133A gene expression datasets. SFG, MSN and LSN, stand for three types of brain tissue samples: Superior Frontal Gyrus, Medial and Lateral Substantia nigra, resp. Broadman Area 9 (BA9), Putamen (PT) and Substantia Nigra (SN) C– Another three affected brain areas:
- 8. Figure 4. Parkinson's disease direct interaction network Node Colors: Blue- genes of interest from the SDEG set Green – known PD genes in SDEGs Interaction colors: regulation – dashed grey, molecular transport – dotted red, co-expression : solid blue, protein modification – solid green, and protein-protein binding – solid purple
- 9. Table 1. Summary of the genes of interest and genes already known in Parkinson disease
- 10. Figure 5. Parkinson's disease compact shortest path network Orange – gene of interest in connecting nodes Red – known genes in connecting nodes Interaction colors: protein modification – solid green, promoter binding – dotted green and direct regulation – solid grey. Key players determined by node degree, closeness centrality, and betweenness centrality (Pajek software)
- 11. Table 2. Gene Ontology enrichment analysis of Parkinson's disease compact shortest path network
- 12. Table 3. Genes of interest for Parkinson’s disease identified by “guilt-by-association” with the known PD-related genes
- 13. Table 4. Enriched KEGG pathways in Parkinson’s from DAVID analysis (Database for Annotation, Visualization and Integrated Discovery )
- 14. Figure 6. Parkinson’s disease enriched canonical pathways as produced by IPA analysis
- 15. Figure 7. Integrated Parkinson’s disease mechanism The 46 genes/proteins found in common in all 16 enriched KEGG pathways Three routes emerged for triggering the Parkinson’s disease mechanism via one of the extra-cellular ligands CX3CL1, IL12B and SEMA6D.
- 16. Figure 8. Parkinson’s disease microRNA regulatory network The genes and miRNAs implicated in PD pathology are highlighted in green and the genes of potential interest are highlighted in blue. Genes that code for transcription factors (TFs) are highlighted in yellow. MiRNA-mRNA target interactions are represented using solid orange lines.
- 17. Table 5. Genes of interest determined from Parkinson's disease microRNA regulatory network
- 18. Summary Using Pathway Studio software and the ResNet database three routes were identified for triggering the Parkinson’s disease molecular mechanism via one of the extra-cellular ligands CX3CL1, IL12B and SEMA6D. 28 genes of potential importance for Parkinson disease are Identified proceeding from their close relations to known Parkinson disease genes in shortest paths type networks. 17 genes of interest were determined from Parkinson's disease microRNA regulatory network. Four of these (PAK1, SYNJ1, UBE2N, NEDD4I) are also present in the above list of 28 genes of interest, which makes these four genes prime candidates for experimental studies. These findings demonstrate the important role Pathway Studio software plays in biomedical research as powerful tool for build-up and analysis of biomolecular networks.
- 19. This study was essential part of the PhD Thesis of my former student Dr. Sreedevi Chandrasekaran, defended in May 2013. Thank You for Your Attention
- 20. Thank you for joining our webinar today: A Network View on Parkinson's Disease with Professor Danail Bonchev If you have any questions for our speaker, please type them into the CHAT window. If you would like more information about Pathway Studio, you can contact: Frank A White III, PhD f.white.1@elsevier.com