This document discusses the role of statisticians in personalized medicine and provides an overview of statistical methods used in bioinformatics. It begins with an introduction to the speaker's educational background and current positions. The rest of the document is outlined as follows: an introduction to personalized medicine and patients' heterogeneity; applications of microarray and next-generation sequencing technologies; statistical methods for microarray data analysis including gene selection, classification, clustering, and dose-response studies; and RNA-seq analysis from sequencing to identifying subtype-specific transcripts. Statistics plays an important role in developing personalized medicine through multidisciplinary collaboration and exploring big data in healthcare.
tranSMART Community Meeting 5-7 Nov 13 - Session 3: tranSMART a Data Warehous...David Peyruc
tranSMART Community Meeting 5-7 Nov 13 - Session 3: tranSMART a Data Warehouse for Translational Medicine at Takeda Pharmaceuticals
International
Dave Marberg, Takeda
We have used the tranSMART platform to construct a warehouse containing data from several
Takeda clinical trials, proprietary preclinical drug activity studies, 1600 Gene Expression
Omnibus studies, and data from TCGA, CCLE, and other sources. All gene expression data has
been globally normalized. We extended the tranSMART platform with a set of R function calls
to enable cross-study queries and analysis via the rich toolset available in R. The utility of the
data warehouse is exemplified by a study in which we built a predictive model for drug
sensitivities. The model was trained on gene expression and IC50 data from cell lines and was
found to correctly predict drug activity in oncology indications.
SCDT: FC-NNC-structured Complex Decision Technique for Gene Analysis Using Fu...IJECEIAES
In many diseases classification an accurate gene analysis is needed, for which selection of most informative genes is very important and it require a technique of decision in complex context of ambiguity. The traditional methods include for selecting most significant gene includes some of the statistical analysis namely 2-Sample-T-test (2STT), Entropy, Signal to Noise Ratio (SNR). This paper evaluates gene selection and classification on the basis of accurate gene selection using structured complex decision technique (SCDT) and classifies it using fuzzy cluster based nearest neighborclassifier (FC-NNC). The effectiveness of the proposed SCDT and FC-NNC is evaluated for leave one out cross validation metric(LOOCV) along with sensitivity, specificity, precision and F1-score with four different classifiers namely 1) Radial Basis Function (RBF), 2) Multi-layer perception(MLP), 3) Feed Forward(FF) and 4) Support vector machine(SVM) for three different datasets of DLBCL, Leukemia and Prostate tumor. The proposed SCDT &FC-NNC exhibits superior result for being considered more accurate decision mechanism.
Genes and Tissue Culture Technology - Next Generation Sequencing - Applicatio...Tiong Qi En
A short presentation on the applications of next generation sequencing in cancer treatment. All content displayed and shared remains the courtesy of Taylor's University. Published 17/10/18.
tranSMART Community Meeting 5-7 Nov 13 - Session 3: tranSMART a Data Warehous...David Peyruc
tranSMART Community Meeting 5-7 Nov 13 - Session 3: tranSMART a Data Warehouse for Translational Medicine at Takeda Pharmaceuticals
International
Dave Marberg, Takeda
We have used the tranSMART platform to construct a warehouse containing data from several
Takeda clinical trials, proprietary preclinical drug activity studies, 1600 Gene Expression
Omnibus studies, and data from TCGA, CCLE, and other sources. All gene expression data has
been globally normalized. We extended the tranSMART platform with a set of R function calls
to enable cross-study queries and analysis via the rich toolset available in R. The utility of the
data warehouse is exemplified by a study in which we built a predictive model for drug
sensitivities. The model was trained on gene expression and IC50 data from cell lines and was
found to correctly predict drug activity in oncology indications.
SCDT: FC-NNC-structured Complex Decision Technique for Gene Analysis Using Fu...IJECEIAES
In many diseases classification an accurate gene analysis is needed, for which selection of most informative genes is very important and it require a technique of decision in complex context of ambiguity. The traditional methods include for selecting most significant gene includes some of the statistical analysis namely 2-Sample-T-test (2STT), Entropy, Signal to Noise Ratio (SNR). This paper evaluates gene selection and classification on the basis of accurate gene selection using structured complex decision technique (SCDT) and classifies it using fuzzy cluster based nearest neighborclassifier (FC-NNC). The effectiveness of the proposed SCDT and FC-NNC is evaluated for leave one out cross validation metric(LOOCV) along with sensitivity, specificity, precision and F1-score with four different classifiers namely 1) Radial Basis Function (RBF), 2) Multi-layer perception(MLP), 3) Feed Forward(FF) and 4) Support vector machine(SVM) for three different datasets of DLBCL, Leukemia and Prostate tumor. The proposed SCDT &FC-NNC exhibits superior result for being considered more accurate decision mechanism.
Genes and Tissue Culture Technology - Next Generation Sequencing - Applicatio...Tiong Qi En
A short presentation on the applications of next generation sequencing in cancer treatment. All content displayed and shared remains the courtesy of Taylor's University. Published 17/10/18.
EFFICACY OF NON-NEGATIVE MATRIX FACTORIZATION FOR FEATURE SELECTION IN CANCER...IJDKP
Over the past few years, there has been a considerable spread of microarray technology in many biological patterns, particularly in those pertaining to cancer diseases like leukemia, prostate, colon cancer, etc. The primary bottleneck that one experiences in the proper understanding of such datasets lies in their dimensionality, and thus for an efficient and effective means of studying the same, a reduction in their dimension to a large extent is deemed necessary. This study is a bid to suggesting different algorithms and approaches for the reduction of dimensionality of such microarray datasets.This study exploits the matrix-like structure of such microarray data and uses a popular technique called Non-Negative Matrix Factorization (NMF) to reduce the dimensionality, primarily in the field of biological data. Classification accuracies are then compared for these algorithms.This technique gives an accuracy of 98%.
EFFICACY OF NON-NEGATIVE MATRIX FACTORIZATION FOR FEATURE SELECTION IN CANCER...IJDKP
Over the past few years, there has been a considerable spread of microarray technology in many
biological patterns, particularly in those pertaining to cancer diseases like leukemia, prostate, colon
cancer, etc. The primary bottleneck that one experiences in the proper understanding of such datasets lies
in their dimensionality, and thus for an efficient and effective means of studying the same, a reduction in
their dimension to a large extent is deemed necessary. This study is a bid to suggesting different algorithms
and approaches for the reduction of dimensionality of such microarray datasets.This study exploits the
matrix-like structure of such microarray data and uses a popular technique called Non-Negative Matrix
Factorization (NMF) to reduce the dimensionality, primarily in the field of biological data. Classification
accuracies are then compared for these algorithms.This technique gives an accuracy of 98%
Deep learning based multi-omics integration, a surveySOYEON KIM
1. Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders, Pacific Symposium on Biocomputing, 2015
2. A deep learning approach for cancer detection and relevant gene identification, Pacific Symposium on Biocomputing, 2016
3. Deep Learning based multi-omics integrationrobustly predicts survival in liver cancer, preprint, 2017
Gene is the basic physical unit of inheritance that passed information from parents to offspring. Genes are arranged, one after another, on structure called chromosome.
A gene is region of DNA that encodes function and chromosome consist of long DNA strands containing many genes.
A human chromosome can have up to 500 million base pair of DNA with thousands of genes.
Technique responsible for correcting the defective genes responsible for disease development is called Gene Therapy
Robust Pathway-based Multi-Omics Data Integration using Directed Random Walk ...SOYEON KIM
17th Annual International Conference on Critical Assessment of Massive Data Analysis (CAMDA 2018)
Cancer Data Integration Challenge (http://camda.info/)
Introduction to Applications of Proteomics Science,
Proteomics- Techniques, Applications of proteomics
Presented by
A. Harsha Vardhan Naidu
Department of Pharmacology
A Classification of Cancer Diagnostics based on Microarray Gene Expression Pr...IJTET Journal
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.
Integrative analysis of transcriptomics and proteomics data with ArrayMining ...Natalio Krasnogor
These slides are part of a presentation I gave on March 2010 at the BioInformatics and Genome Research Open Club at the Weizmann Institute of Science, Israel.
In these slides my student and I describe two web-applications for microarray and gene/protein set analysis,
ArrayMining.net and TopoGSA. These use ensemble and consensus methods as well as the
possibility of modular combinations of different analysis techniques for an integrative view of
(microarray-based) gene sets, interlinking transcriptomics with proteomics data sources. This integrative process uses tools from different fields, e.g. statistics, optimisation and network
topological studies. As an example for these integrative techniques, we use a microarray
consensus-clustering approach based on Simulated Annealing, which is part of the ArrayMining.net
Class Discovery Analysis module, and show how this approach can be combined in a modular
fashion with a prior gene set analysis. The results reveal that improved cluster validity indices can be obtained by merging the two methods, and provide pointers to distinct sub-classes within pre-defined tumour categories for a breast cancer dataset by the Nottingham Queens Medical Centre.
In the second part of the talk, I show how results from a supervised
microarray feature selection analysis on ArrayMining.net can be investigated in further detail with
TopoGSA, a new web-tool for network topological analysis of gene/protein sets mapped on a
comprehensive human protein-protein interaction network. I discuss results from a TopoGSA
analysis of the complete set of genes currently known to be mutated in cancer.
Addressing Questions & Unmet Needs in Melanoma Research and TreatmentTom Williams
The landscape for melanoma research and treatment has rapidly changed over the last decade. Since 2011, the FDA has approved 12 new melanoma treatment regimens – including new classes of drugs that are molecularly targeted therapies (BRAF/MEK inhibitors), immune checkpoint inhibitors (anti CTLA-4, PD-1/PD-L1) and other immunotherapies (e.g. T-Vec). Scientists have also unraveled many of the genomic mutations found in the most common form, cutaneous melanoma, melanoma that arises primarily on sun-exposed areas of the skin. With these advances in research and treatment, the key unanswered questions have changed rapidly and existing preclinical models may not be sufficient to answer such questions surrounding immune checkpoint inhibition; resistance development, comparing to cuaneous melanoma, and how to improve early detection.
Importantly, there are no models that accurately predict the patient journey. New models and additional research is needed to more fully represent all melanoma subtypes, stages, or treatment responses.
About the speakers:
Marc Hurlbert, Ph.D. Chief Science Officer, Melanoma Research Alliance. Marc is currently responsible for guiding MRA’s scientific strategy, overseeing the peer-reviewed grant-making program, and forging scientific collaborations. He has more than 18 years of nonprofit and grant-making experience focused on advancing medical research. Past work has included treatment and prevention strategies for breast cancer, lymphoma and multiple myeloma, as well as juvenile diabetes.
Tom Williams, PhD, Life Sciences Professional Services Project Manager, Elsevier. Tom is a Life Sciences Knowledge Manager and Research Scientist. with extensive experience as an academic researcher in neurodegeneration and Alzheimer’s disease. He is also in skilled biophysical chemistry, dementia disorders, and biochemistry; and the author of many publications in the field of Alzheimer’s disease.
Introduction to Cytoscape talk given in March 2010 at the CRUK CRI. Cambridge UK.
It was design to give a broad introduction the features available in Cytoscape for wet lab researchers.
Andy Jarvis PARASID Near Real Time Monitoring Of Habitat Change Using A Neur...CIAT
Brown bag presentation for TNC in Washington 24th September 2009 on the PARASID habitat monitoring tool. Authored by Andy Jarvis, Louis Reymondin and Jerry Touval.
EFFICACY OF NON-NEGATIVE MATRIX FACTORIZATION FOR FEATURE SELECTION IN CANCER...IJDKP
Over the past few years, there has been a considerable spread of microarray technology in many biological patterns, particularly in those pertaining to cancer diseases like leukemia, prostate, colon cancer, etc. The primary bottleneck that one experiences in the proper understanding of such datasets lies in their dimensionality, and thus for an efficient and effective means of studying the same, a reduction in their dimension to a large extent is deemed necessary. This study is a bid to suggesting different algorithms and approaches for the reduction of dimensionality of such microarray datasets.This study exploits the matrix-like structure of such microarray data and uses a popular technique called Non-Negative Matrix Factorization (NMF) to reduce the dimensionality, primarily in the field of biological data. Classification accuracies are then compared for these algorithms.This technique gives an accuracy of 98%.
EFFICACY OF NON-NEGATIVE MATRIX FACTORIZATION FOR FEATURE SELECTION IN CANCER...IJDKP
Over the past few years, there has been a considerable spread of microarray technology in many
biological patterns, particularly in those pertaining to cancer diseases like leukemia, prostate, colon
cancer, etc. The primary bottleneck that one experiences in the proper understanding of such datasets lies
in their dimensionality, and thus for an efficient and effective means of studying the same, a reduction in
their dimension to a large extent is deemed necessary. This study is a bid to suggesting different algorithms
and approaches for the reduction of dimensionality of such microarray datasets.This study exploits the
matrix-like structure of such microarray data and uses a popular technique called Non-Negative Matrix
Factorization (NMF) to reduce the dimensionality, primarily in the field of biological data. Classification
accuracies are then compared for these algorithms.This technique gives an accuracy of 98%
Deep learning based multi-omics integration, a surveySOYEON KIM
1. Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders, Pacific Symposium on Biocomputing, 2015
2. A deep learning approach for cancer detection and relevant gene identification, Pacific Symposium on Biocomputing, 2016
3. Deep Learning based multi-omics integrationrobustly predicts survival in liver cancer, preprint, 2017
Gene is the basic physical unit of inheritance that passed information from parents to offspring. Genes are arranged, one after another, on structure called chromosome.
A gene is region of DNA that encodes function and chromosome consist of long DNA strands containing many genes.
A human chromosome can have up to 500 million base pair of DNA with thousands of genes.
Technique responsible for correcting the defective genes responsible for disease development is called Gene Therapy
Robust Pathway-based Multi-Omics Data Integration using Directed Random Walk ...SOYEON KIM
17th Annual International Conference on Critical Assessment of Massive Data Analysis (CAMDA 2018)
Cancer Data Integration Challenge (http://camda.info/)
Introduction to Applications of Proteomics Science,
Proteomics- Techniques, Applications of proteomics
Presented by
A. Harsha Vardhan Naidu
Department of Pharmacology
A Classification of Cancer Diagnostics based on Microarray Gene Expression Pr...IJTET Journal
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.
Integrative analysis of transcriptomics and proteomics data with ArrayMining ...Natalio Krasnogor
These slides are part of a presentation I gave on March 2010 at the BioInformatics and Genome Research Open Club at the Weizmann Institute of Science, Israel.
In these slides my student and I describe two web-applications for microarray and gene/protein set analysis,
ArrayMining.net and TopoGSA. These use ensemble and consensus methods as well as the
possibility of modular combinations of different analysis techniques for an integrative view of
(microarray-based) gene sets, interlinking transcriptomics with proteomics data sources. This integrative process uses tools from different fields, e.g. statistics, optimisation and network
topological studies. As an example for these integrative techniques, we use a microarray
consensus-clustering approach based on Simulated Annealing, which is part of the ArrayMining.net
Class Discovery Analysis module, and show how this approach can be combined in a modular
fashion with a prior gene set analysis. The results reveal that improved cluster validity indices can be obtained by merging the two methods, and provide pointers to distinct sub-classes within pre-defined tumour categories for a breast cancer dataset by the Nottingham Queens Medical Centre.
In the second part of the talk, I show how results from a supervised
microarray feature selection analysis on ArrayMining.net can be investigated in further detail with
TopoGSA, a new web-tool for network topological analysis of gene/protein sets mapped on a
comprehensive human protein-protein interaction network. I discuss results from a TopoGSA
analysis of the complete set of genes currently known to be mutated in cancer.
Addressing Questions & Unmet Needs in Melanoma Research and TreatmentTom Williams
The landscape for melanoma research and treatment has rapidly changed over the last decade. Since 2011, the FDA has approved 12 new melanoma treatment regimens – including new classes of drugs that are molecularly targeted therapies (BRAF/MEK inhibitors), immune checkpoint inhibitors (anti CTLA-4, PD-1/PD-L1) and other immunotherapies (e.g. T-Vec). Scientists have also unraveled many of the genomic mutations found in the most common form, cutaneous melanoma, melanoma that arises primarily on sun-exposed areas of the skin. With these advances in research and treatment, the key unanswered questions have changed rapidly and existing preclinical models may not be sufficient to answer such questions surrounding immune checkpoint inhibition; resistance development, comparing to cuaneous melanoma, and how to improve early detection.
Importantly, there are no models that accurately predict the patient journey. New models and additional research is needed to more fully represent all melanoma subtypes, stages, or treatment responses.
About the speakers:
Marc Hurlbert, Ph.D. Chief Science Officer, Melanoma Research Alliance. Marc is currently responsible for guiding MRA’s scientific strategy, overseeing the peer-reviewed grant-making program, and forging scientific collaborations. He has more than 18 years of nonprofit and grant-making experience focused on advancing medical research. Past work has included treatment and prevention strategies for breast cancer, lymphoma and multiple myeloma, as well as juvenile diabetes.
Tom Williams, PhD, Life Sciences Professional Services Project Manager, Elsevier. Tom is a Life Sciences Knowledge Manager and Research Scientist. with extensive experience as an academic researcher in neurodegeneration and Alzheimer’s disease. He is also in skilled biophysical chemistry, dementia disorders, and biochemistry; and the author of many publications in the field of Alzheimer’s disease.
Introduction to Cytoscape talk given in March 2010 at the CRUK CRI. Cambridge UK.
It was design to give a broad introduction the features available in Cytoscape for wet lab researchers.
Andy Jarvis PARASID Near Real Time Monitoring Of Habitat Change Using A Neur...CIAT
Brown bag presentation for TNC in Washington 24th September 2009 on the PARASID habitat monitoring tool. Authored by Andy Jarvis, Louis Reymondin and Jerry Touval.
Microarray data and pathway analysis: example from the benchMaté Ongenaert
Microarray data and pathway analysis: example from the bench
by drs. Jolien Vermeire - HIVlab, Department of Clinical Chemistry, Microbiology and Immunology – UGent
The increased availability and lower cost of gene expression microarrays has stimulated the use of transcriptome studies in a high variety of fields. Generating expression data at whole-genome level can indeed be a powerful method to characterize cellular pathways involved in a certain biological process. However, the challenge of extracting relevant biological information from such large datasets still prevents researchers from exploiting this tool. In this presentation I will share my personal experience, as a 'researcher non-bioinformatician', with performing microarray data and pathway analyses. I will give a general overview of the different steps that where followed in order to transform raw gene expression data, obtained in context of HIV research, into useful biological information and highlight different methods and software tools that helped me in this process.
The Functional and Pathway Analysis talk given in March 2010 at the CRUK CRI. Cambridge UK.
It was designed to introduce wet-lab researchers to using web-based tools for doing functional analysis of gene lists, such as from microarray experiments.
Molecular Subtyping of Breast Cancer and Somatic Mutation Discovery Using DNA...Setia Pramana
Molecular Subtyping of Breast Cancer and Somatic Mutation Discovery Using DNA and RNA sequence
Guess Lecture at Computer Science Department, IPB, Bogor
dkNET Webinar: Multi-Omics Data Integration for Phenotype Prediction of Type-...dkNET
Abstract
Omics techniques (e.g., i.e., transcriptomics, genomics, and epigenomics) report quantitative measures of more than tens of thousands of biological features and provide a more comprehensive molecular perspective of studied diabetes mechanisms compared to transitional approaches. Identifying representative molecular signatures from the tremendous number of biological features becomes a central problem in utilizing the data for clinical decision-making. Exploring the complex causal relations of the identified representative molecular signatures and diabetes phenotypes can be the most effective and efficient ways to improve the understanding of diabetes and assess the cause of diabetes for the new patients with already collected data influencing (e.g., TEDDY project). However, due to the unavoidable patient heterogeneity, statistical randomness, and experimental noise in the high-dimension, low-sample-size omics data of the diabetic patients, utilizing the available data for clinical decision-making remains an ongoing challenge for many researchers. To overcome the limitations, in this study we developed (1) a generative adversarial network (GAN)-based model to generate synthetic omics data for the samples with few omics profiles available; (2) a deep learning-based fusion network model for phenotype prediction of type-1 diabetes; (3) a long short-term memory (LSTM)-based model for predicting outcomes of islet autoantibody and persistent positivity. The models are tested on the multi-omics data in TEDDY project.
Presenter: Wei Zhang, Ph.D. Assistant Professor, Department of Computer Science & Genomics and Bioinformatics Cluster, University of Central Florida
Upcoming webinars schedule: https://dknet.org/about/webinar
Utilization of NGS to Identify Clinically-Relevant Mutations in cfDNA: Meet t...QIAGEN
Pancreatic cancer is a uniquely lethal malignancy characterized by frequent mutations in KRAS, CDKN2A, SMAD4, TP53 and many others. We have shown that KRAS mutation can be detected in cell-free, circulating tumor DNA (ctDNA) isolated from the plasma in a subset of patients and is associated with poor prognosis. The ability to simultaneously detect multiple pancreatic cancer-specific mutations in ctDNA would open a new avenue for detection of clinically-relevant mutations. In this study, we performed ultra-deep sequencing of ctDNA from advanced pancreatic cancer patients prior to treatment with Gemcitabine and Erlotinib following target enrichment. Somatic, non-synonymous variants were identified in 29 different genes at allele frequencies typically less than 0.5%. Updated results of ultra-deep NGS analysis will be presented.
Los días 11 y 12 de diciembre de 2014, la Fundación Ramón Areces celebró el Simposio Internacional 'Neuropatías periféricas hereditarias. Desde la biología a la terapéutica' en colaboración con CIBERER-ISCIII y el Centro de Investigación Príncipe Felipe. El tipo más común de estas patologías es la enfermedad de Charcot-Marie-Tooth, un trastorno neuromuscular hereditario con una prevalencia estimada de 17-40 afectados por 100.000 habitantes. Durante estos dos días, investigadores mostraron sus avances en la mejora del diagnóstico y el tratamiento y, por ende, de la aproximación clínica y la calidad de vida de las personas afectadas por estas patologías.
Golden Helix’s SNP & Variation Suite (SVS) has been used by researchers around the world to do trait analysis and association testing on large cohorts of samples in both humans and other species. As Next-Generation Sequencing of whole genomes becomes more affordable, large cohorts of Whole Genome Sequencing (WGS) samples are available to search for additional trait association signals that were not found in array-based testing. In fact, recent papers have shown that WGS analysis using advanced GREML (Genomic Relatedness Restricted Maximum Likelihood) techniques is able to outperform micro-array based GWAS methods in the analysis of complex traits and proportion of the trait heritability explained.
Our latest update release of SVS has expanded the exiting maximum likelihood and GRM methods to support these new techniques. We have also enhanced various other association testing and prediction methodologies. This webcast showcases:
- Newly supported analysis workflow for whole genome variants using LD binning and enhanced GBLUP analysis
- Enhanced gender correction using REML
- Additional capabilities for genomic prediction and phenotype prediction
We are continually improving our products based on our customer’s feedback. We hope you enjoy this recording highlighting the exciting new features and select enhancements we have made.
Big Data and Genomic Medicine by Corey NislowKnome_Inc
View the webinar at: http://www.knome.com/webinar-big-data-genomic-medicine. This presentation covers an overview of genomic medicine, requirements and challenges of next-generation sequencing, bottlenecks to broader healthcare adoption, and why “we want to sequence everyone.”
Kehidupan sehari-hari dengan Personnummer atau SIN Single Identity NumberSetia Pramana
“Kehidupan sehari-hari dengan Personnummer atau SIN Single Identity Number” oleh Bapak Janto Marzuki (Former Database Manager Ericsson Sweden), Diskusi PPI Stockholm dan SIS: “Single Identification Number in Sweden, its impact on social life and research”
Research possibilities with the Personal Identification Number (person nummer...Setia Pramana
Research possibilities with the Personal Identification Number (person nummer) in Sweden by Prof. Marie Reilly, given at discussion on Single Identification Number in Sweden, its impact on social life and research, Stockholm, September 22, 2013
Developing R Graphical User Interfaces, presented at
1. Workshop on Development of R software for data analysis, Hasselt University, Belgium, March 13th, 2013.
2. Joint Seminar, Medical Epidemiology and Biostatistics Department, Karolinska Institutet, April 4th, 2013.
Model averaging in dose-response study in microarray expressionSetia Pramana
Dose-response studies recently have been integrated with microarray technologies. Within this setting, the response is gene-expression measured at a certain dose level. In this study, genes which are not differentially expressed are filtered out using a monotonic trend test. Then for the genes with significant monotone trend, several dose-response models were fitted. Afterward model averaging technique is carried for estimating the of target dose, ED50.
Presented in All models are wrong...
Model uncertainty & selection in complex models workshop, Groningen 14-16 march 2011
Dose-Response Modeling of Gene Expression Data in pre-clinical Microarray Exp...Setia Pramana
Dose-Response Modeling of Gene Expression Data in pre-clinical Microarray Experiments. "Dose-Response Modeling of Gene Expression Data in pre-clinical Microarray Experiments".
Presented in Workshop on Multiplicity and Microarray Analysis, Hasselt University, Diepenbeek Belgium on February 19, 2010
IsoGeneGUI: a graphical user interface for analyzing dose-response studies in microarray experiments".
Presented in the R user conference 2010, National Institute of Standards and Technology (NIST), Gaithersburg, Maryland, USA, July 21, 2010
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
The Role of Statistician in Personalized Medicine: An Overview of Statistical Methods in Bioinformatics
1. The Role of The Statisticians in
Personalized Medicine:
An Overview of Statistical
Methods in Bioinformatics
Setia Pramana
Teknik Fisika
Fakultas Teknik Industri
Institut Teknologi Sepuluh Nopember
Surabaya, 12 March 2014
Setia Pramana 1
2. Educational Background
• Universitas Brawijaya Malang, FMIPA, Statistics
department, 1995-1999.
• Hasselt Universiteit, Belgium, MSc in Applied Statistics
2005-2006.
• Hasselt Universiteit, Belgium, MSc in Biostatistics 2006-
2007.
• Hasselt Universiteit, Belgium, PhD Statistical
Bioinformatics, 2007-2011.
• Medical Epidemiology And Biostatistics Dept. Karolinska
Institutet, Sweden, Postdoctoral, 2011-2014
3. Now?
• Lecture and Researcher at Sekolah Tinggi Ilmu
Statistik, Jakarta.
• Adjunct Faculty at Medical Epidemiology and
Biostatistics Dept, Karolinska Institutet, Stockholm.
5. Personalized Medicine
• Drug Development:
– Takes 10-15 years
– Cost millions USD
• Who: Pharmaceutical, biotechnology, device companies,
Universities and government research agencies
• Regulatory: The US Food and Drug Administration (FDA)
• Evaluate:
– Safety – can people take it?
– Efficacy – does it do anything in humans?
– Effectiveness – is it better or at least as good as what is
currently available?
– Do the benefits outweigh the risks?
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6. Personalized Medicine
• Drug Development Stages:
- Drug Discovery
- Pre-clinical Development
- Clinical Development 4 Phases
• Statisticians are involved in all stages
• Stages are highly regulated
• Result is based on most of patients
• But .. Patients are created differently!
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8. Patients Heterogeneity
• We’re all different in
- Physiological, demographic characteristics
- Medical history
- Genetic/genomic characteristics
• What works for a patient with one set of
characteristics might not work for another!
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9. Patients Heterogeneity
• “One size does not fit all”
• Use a patient’s characteristics to determine best
treatment for him/her
• Genomic information is a great potential
-- > Personalized medicine:
“The right treatment for the right patient at the right
time”
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10. Subgroup identification and targeted treatment
• Determine subgroups of patients who share certain
characteristics and would get better on a particular
treatment
• Discover biomarkers which can identify the subgroup
• Focus on finding and treating a subgroup
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11. Subgroup identification and targeted treatment
Genotype Phenotype Intervention Outcome
Mutations/SN
Ps
Gene/Protein
Expression
Epigenetics
Diseases
Disability
etc
Drug
Regimes
Personalized
medicine
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12. Advanced Biomedical Technologies
• High-throughput microarrays and molecular imaging
to monitor SNPs, gene and protein expressions
• Next-Generation Sequencing
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15. Gene
• The full DNA sequence of an organism is called its
genome
• A gene is a segment that specifies the sequence of
one or more protein.
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16. Genomics
• The study of all the genes of a cell, or tissue, at :
– the DNA (genotype), e.g., GWAS SNP, CNV etc…
– mRNA (transcriptomics), Gene expression,
– or protein levels (proteomics).
• Functional Genomics: study the functionality of specific
genes, their relations to diseases, their associated
proteins and their participation in biological processes.
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17. Microarray
• DNA microarrays are biotechnologies which
allow the monitoring of expression of
thousand genes.
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18. Applications
• High efficacy and low/no side effect drug
• Genes related disease.
• Biological discovery
– new and better molecular diagnostics
– new molecular targets for therapy
– finding and refining biological pathways
• Molecular diagnosis of leukemia, breast cancer, etsc.
• Appropriate treatment for genetic signature
• Potential new drug targets
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19. Microarray
Overview of the process
of generating high
throughput gene
expression data using
microarrays.
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20. The Pipeline
• Experiment design Lab work Image processing
• Signal summarization (RMA, GCRMA)
• Normalization
• Data Analysis:
– Differentially Expressed genes
– Clustering
– Classification
– Etc.
• Network / Pathways (GSEA etc..)
• Biological interpretations
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23. Challenges
• Mega data, difficult to visualize
• Too few records (columns/samples), usually < 100
• Too many rows(genes), usually > 10,000
• Too many genes likely leading to False positives
• For exploration, a large set of all relevant genes is
desired
• For diagnostics or identification of therapeutic
targets, the smallest set of genes is needed
• Model needs to be explainable to biologists
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27. Clustering
• Cluster the genes
• Cluster the
arrays/conditions
• Cluster both simultaneously
• K-means
• Hierarchical
• Biclustering algorithms
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28. Clustering
• Cluster or Classify
genes according to
tumors
• Cluster tumors
according to genes
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29.
30. Classification
• Linear Discriminat Analysis
• K nearest neighbour
• Logistic regression
• L1 Penalized Logistric regression
• Neural Network
• Support vector machines
• Random forest
• etc
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31. Aim: To improve understanding of host protein
profiles during disease progression especially in
children.
32. Classification of Malaria Subtypes
•Identify panel of proteins which could distinguish
between different subtypes.
•Implement L1-penalized logistic regression
33. Penalized Logistic Regression
•Logistic regression is a supervised method for binary
or multi-class classification.
•In high-dimensional data (e.g., microarray): More
variables than the observations Classical logistic
regression does not work.
•Other problems: Variables are correlated
(multicolinierity) and over fitting.
•Solution: Introduce a penalty for complexity in the
model.
36
35. • Shrinks all regression coefficients () toward zero
and set some of them to zero.
• Performs parameter estimation and variable
selection at the same time.
• The choice of λ is crucial and chosen via k-fold
cross-validation procedure.
• The procedure is implemented in an R package
called penalized.
38
L1 Penalized Logistic Regression
50. Subtype-specific Transcripts/Isoforms
• Breast invasive carcinoma (BRCA) from the Cancer
Genome Atlas Project (TCGA).
• 329 tumor samples.
• Platform: illumina
• Paired-end reads (length 50 bp).
• 20 -100 million reads
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51. Subtype-specific Transcripts/Isoforms
• To discover transcripts/isoforms which are only
significantly (high/low) expressed in a certain cancer
subtype.
Pramana, et.al 55NBBC 2013
52. Analysis Flow
329 samples TCGA
Discovery set
179 samples
Validation set
- TCGA 150 samples
- External samples
Classification to mol-subtypes
- Use Swedish microarray data as
training data.
- Based on gene level FPKM
- Median and variance normalization
- K-nearest neighbor
- Classifier genes selection
Subtype-specific Transcript
- Transcript level FPKM of all
genes
- For each transcript: Robust
contrast tests.
- Multiple testing adjustment.
Pramana, et.al 56NBBC 2013
56. Software?
• R now is growing, especially in bioinformatics
– Statistics, data analysis, machine learning
– Free
– High Quality
– Open Source
– Extendable (you can submit and publish your own package!!)
– Can be integrated with other languages (C/C++, Java, Python)
– Large active user community
– Command-based (-)
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57. Summary
• Statistics plays important roles in developing
personalized medicine
• Multidisciplinary field need collaboration with
different experts.
• Bioinformaticians is one of the sexiest job
• Big Data in Medicine: Numerous opportunities to be
explored and discovered.
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