This document provides a summary of Konstantinos Lykostratis's professional experience and qualifications. It outlines his experience as a project manager and systems biology researcher, including his roles developing software solutions and mathematical models. It also lists his educational background, obtaining a PhD in computational systems biology and bioinformatics from University College London, and a BSc in human genetics from the University of Leicester.
Mastering RNA-Seq (NGS Data Analysis) - A Critical Approach To Transcriptomic...Elia Brodsky
This workshop will address critical issues related to Transcriptomics data:
Processing raw Next Generation Sequencing (NGS) data:
1. Next Generation Sequencing data preprocessing:
Trimming technical sequences
Removing PCR duplicates
2. RNA-seq based quantification of expression levels:
Conventional pipelines (looking at known transcripts)
Identification of novel isoforms
Analysis of Expression Data Using Machine Learning:
3. Unsupervised analysis of expression data:
Principal Component Analysis
Clustering
4. Supervised analysis:
Differential expression analysis
Classification, gene signature construction
5. Gene set enrichment analysis
The workshop will include hands-on exercises utilizing public domain datasets:
breast cancer cell lines transcriptomic profiles (https://genomebiology.biomedcentral.com/articles/10.1186/gb-2013-14-10-r110),
patient-derived xenograft (PDX) mouse model of tumor and stroma transcriptomic profiles (http://www.oncotarget.com/index.php?journal=oncotarget&page=article&op=view&path[]=8014&path[]=23533), and
processed data from The Cancer Genome Atlas samples (https://cancergenome.nih.gov/).
Team: The workshops are designed by the researchers at the Tauber Bioinformatics Research Center at University of Haifa, Israel in collaboration with academic centers across the US. Technical support for the workshops is provided by the Pine Biotech team. https://edu.t-bio.info/a-critical-approach-to-transcriptomic-data-analysis/
The National Resource for Network Biology (NRNB) held its External Advisory Council meeting on December 12, 2012. The NRNB is focused on developing network biology tools and collaborating with investigators. It oversees various technology research and development projects, software releases including Cytoscape 3.0, collaboration projects, and outreach/training events. The meeting agenda covered progress updates and sought advice on future plans.
Semantic Technology empowering Real World outcomes in Biomedical Research and...Amit Sheth
Based on the context and background knowledge:
- Patient has leg swelling and stiffness which is limiting her function
- She also has shortness of breath
- Shortness of breath can be a symptom of heart failure
- Heart failure can cause leg swelling
The NLP should annotate:
Problem: Heart failure
Symptom: Shortness of breath
Symptom: Leg swelling
By utilizing background knowledge, the inconsistency is resolved.
Resolve Inconsistency
The patient reports intermittent chest pain NLP Patient has chest pain
on exertion for the past few months. On
exam today, she denies any chest pain.
Evolutionary Algorithms for Self-Organising SystemsNatalio Krasnogor
Talk I gave at Ben Gurion University of the Negev in Israel on the 24rd/June/2009. These are a series of talks for the period in which I visited BGU as a distinguished visiting scientist
The National Resource for Network Biology aims to provide freely available, open-source software tools to enable researchers to assemble biological data into networks and pathways and use these networks to better understand biological systems and disease; it pursues this mission through technology research and development projects, driving biological projects, collaboration and service projects, training, and dissemination; key components include the Cytoscape software platform, supercomputing infrastructure, and partnerships with over 30 external research groups.
The NRNB has been funded as an NIGMS Biomedical Technology Research Resource since 2010. During the previous five-year period, NRNB investigators introduced a series of innovative methods for network biology including network-based biomarkers, network-based stratification of genomes, and automated inference of gene ontologies using network data. Over the next five years, we will seek to catalyze major phase transitions in how biological networks are represented and used, working across three broad themes: (1) From static to differential networks, (2) From descriptive to predictive networks, and (3) From flat to hierarchical networks bridging across scales. All of these efforts leverage and further support our growing stable of network technologies, including the popular Cytoscape network analysis infrastructure.
This doctoral dissertation from Vilnius Gediminas Technical University investigates the efficient implementation of lattice-ladder multilayer perceptrons (LLMLPs) in field programmable gate arrays (FPGAs) for real-time intelligent systems such as speech recognition. The dissertation develops an optimization technique for implementing LLMLPs on FPGAs based on specialized efficiency criteria. It also develops and analyzes optimized FPGA intellectual property cores for feature extraction and comparison in a Lithuanian speech recognizer. Experimental tests evaluate the accuracy and speed of the speech recognizer using the developed LLMLP cores under different conditions.
National Resource for Networks Biology's TR&D Theme 1: In this theme, we will develop a series of tools and methodologies for conducting differential analyses of biological networks perturbed under multiple conditions. The novel algorithmic methodologies enable us to make use of high-throughput proteomic level data to recover biological networks under specific biological perturbations. The software tools developed in this project enable researchers to further predict, analyze, and visualize the effects of these perturbations and alterations, while enabling researchers to aggregate additional information regarding the known roles of the involved interactions and their participants.
Mastering RNA-Seq (NGS Data Analysis) - A Critical Approach To Transcriptomic...Elia Brodsky
This workshop will address critical issues related to Transcriptomics data:
Processing raw Next Generation Sequencing (NGS) data:
1. Next Generation Sequencing data preprocessing:
Trimming technical sequences
Removing PCR duplicates
2. RNA-seq based quantification of expression levels:
Conventional pipelines (looking at known transcripts)
Identification of novel isoforms
Analysis of Expression Data Using Machine Learning:
3. Unsupervised analysis of expression data:
Principal Component Analysis
Clustering
4. Supervised analysis:
Differential expression analysis
Classification, gene signature construction
5. Gene set enrichment analysis
The workshop will include hands-on exercises utilizing public domain datasets:
breast cancer cell lines transcriptomic profiles (https://genomebiology.biomedcentral.com/articles/10.1186/gb-2013-14-10-r110),
patient-derived xenograft (PDX) mouse model of tumor and stroma transcriptomic profiles (http://www.oncotarget.com/index.php?journal=oncotarget&page=article&op=view&path[]=8014&path[]=23533), and
processed data from The Cancer Genome Atlas samples (https://cancergenome.nih.gov/).
Team: The workshops are designed by the researchers at the Tauber Bioinformatics Research Center at University of Haifa, Israel in collaboration with academic centers across the US. Technical support for the workshops is provided by the Pine Biotech team. https://edu.t-bio.info/a-critical-approach-to-transcriptomic-data-analysis/
The National Resource for Network Biology (NRNB) held its External Advisory Council meeting on December 12, 2012. The NRNB is focused on developing network biology tools and collaborating with investigators. It oversees various technology research and development projects, software releases including Cytoscape 3.0, collaboration projects, and outreach/training events. The meeting agenda covered progress updates and sought advice on future plans.
Semantic Technology empowering Real World outcomes in Biomedical Research and...Amit Sheth
Based on the context and background knowledge:
- Patient has leg swelling and stiffness which is limiting her function
- She also has shortness of breath
- Shortness of breath can be a symptom of heart failure
- Heart failure can cause leg swelling
The NLP should annotate:
Problem: Heart failure
Symptom: Shortness of breath
Symptom: Leg swelling
By utilizing background knowledge, the inconsistency is resolved.
Resolve Inconsistency
The patient reports intermittent chest pain NLP Patient has chest pain
on exertion for the past few months. On
exam today, she denies any chest pain.
Evolutionary Algorithms for Self-Organising SystemsNatalio Krasnogor
Talk I gave at Ben Gurion University of the Negev in Israel on the 24rd/June/2009. These are a series of talks for the period in which I visited BGU as a distinguished visiting scientist
The National Resource for Network Biology aims to provide freely available, open-source software tools to enable researchers to assemble biological data into networks and pathways and use these networks to better understand biological systems and disease; it pursues this mission through technology research and development projects, driving biological projects, collaboration and service projects, training, and dissemination; key components include the Cytoscape software platform, supercomputing infrastructure, and partnerships with over 30 external research groups.
The NRNB has been funded as an NIGMS Biomedical Technology Research Resource since 2010. During the previous five-year period, NRNB investigators introduced a series of innovative methods for network biology including network-based biomarkers, network-based stratification of genomes, and automated inference of gene ontologies using network data. Over the next five years, we will seek to catalyze major phase transitions in how biological networks are represented and used, working across three broad themes: (1) From static to differential networks, (2) From descriptive to predictive networks, and (3) From flat to hierarchical networks bridging across scales. All of these efforts leverage and further support our growing stable of network technologies, including the popular Cytoscape network analysis infrastructure.
This doctoral dissertation from Vilnius Gediminas Technical University investigates the efficient implementation of lattice-ladder multilayer perceptrons (LLMLPs) in field programmable gate arrays (FPGAs) for real-time intelligent systems such as speech recognition. The dissertation develops an optimization technique for implementing LLMLPs on FPGAs based on specialized efficiency criteria. It also develops and analyzes optimized FPGA intellectual property cores for feature extraction and comparison in a Lithuanian speech recognizer. Experimental tests evaluate the accuracy and speed of the speech recognizer using the developed LLMLP cores under different conditions.
National Resource for Networks Biology's TR&D Theme 1: In this theme, we will develop a series of tools and methodologies for conducting differential analyses of biological networks perturbed under multiple conditions. The novel algorithmic methodologies enable us to make use of high-throughput proteomic level data to recover biological networks under specific biological perturbations. The software tools developed in this project enable researchers to further predict, analyze, and visualize the effects of these perturbations and alterations, while enabling researchers to aggregate additional information regarding the known roles of the involved interactions and their participants.
Summary: ENViz performs enrichment analysis for pathways and gene ontology (GO) terms in matched datasets of multiple data types (e.g. gene expression and metabolites or miRNA), then visualizes results as a Cytoscape network that can be navigated to show data overlaid on pathways and GO DAGs.
Background: Modern genomic, metabolomics, and proteomic assays produce multiplexed measurements that characterize molecular composition and biological activity from complimentary angles. Integrative analysis of such measurements remains a challenge to life science and biomedical researchers. We present an enrichment network approach to jointly analyzing two types of sample matched datasets and systematic annotations, implemented as a plugin to the Cytoscape [1] network biology software platform.
Approach: ENViz analyses a primary dataset (e.g. gene expression) with respect to a ‘pivot’ dataset (e.g. miRNA expression, metabolomics or proteomics measurements) and primary data annotation (e.g. pathway or GO). For each pivot entity, we rank elements of the primary data based on the correlation to the pivot across all samples, and compute statistical enrichment of annotation sets in the top of this ranked list based on minimum hypergeometric statistics [2]. Significant results are represented as an enrichment network - a bipartite graph with nodes corresponding to pivot and annotation entities, and edges corresponding to pivot-annotation pairs with statistical enrichmentscores above the user defined threshold. Correlations of primary data and pivot data are visually overlaid on biological pathways for significant pivot-annotation pairs using the WikiPathways resource [3], and on gene ontology terms. Edges of the enrichment network may point to functionally relevant mechanisms. In [4], a significant association between miR-19a and the cell-cycle module was substantiated as an association to proliferation, validated using a high-throughput transfection assay. The figures below show a pathway enrichment network, with pathway nodes green and miRNAs gray (left), network view of the edge between Inflammatory Response Pathway and mir-337-5p (center), and GO enrichment network with red areas indicating high enrichment for immune response and metabolic processes (right).
This document summarizes the accomplishments of the National Resource for Network Biology over a reporting period. It lists numerous quantitative metrics of success, including over 100 publications citing their grants, thousands of daily downloads and uses of their software tools, and training over 100 users. It also provides details on improvements and developments made to several of their modeling frameworks, algorithms, and software applications. Finally, it outlines the formation of a new working group on single-cell RNA-seq analysis and visualization, and improvements made to their computing infrastructure.
New Research Articles 2019 October Issue Signal & Image Processing An Interna...sipij
Signal & Image Processing: An International Journal (SIPIJ)
ISSN: 0976 – 710X [Online]; 2229 - 3922 [Print]
http://www.airccse.org/journal/sipij/index.html
Current Issue; October 2019, Volume 10, Number 5
Free- Reference Image Quality Assessment Framework Using Metrics Fusion and Dimensionality Reduction
Besma Sadou1, Atidel Lahoulou2, Toufik Bouden1, Anderson R. Avila3, Tiago H. Falk3 and Zahid Akhtar4, 1Non Destructive Testing Laboratory, University of Jijel, Algeria, 2LAOTI laboratory, University of Jijel, Algeria, 3University of Québec, Canada and 4University of Memphis, USA
Test-cost-sensitive Convolutional Neural Networks with Expert Branches
Mahdi Naghibi1, Reza Anvari1, Ali Forghani1 and Behrouz Minaei2, 1Malek-Ashtar University of Technology, Iran and 2Iran University of Science and Technology, Iran
Robust Image Watermarking Method using Wavelet Transform
Omar Adwan, The University of Jordan, Jordan
Improvements of the Analysis of Human Activity Using Acceleration Record of Electrocardiographs
Itaru Kaneko1, Yutaka Yoshida2 and Emi Yuda3, 1&2Nagoya City University, Japan and 3Tohoku University, Japan
http://www.airccse.org/journal/sipij/vol10.html
The National Resource for Network Biology (NRNB) aims to advance network biology science through bioinformatic methods, software, infrastructure, collaboration, and training. In the past year, the NRNB made progress in its specific aims, including developing new network analysis methods, catalyzing changes in network representation, establishing software and databases, engaging in collaborations, and providing training opportunities. Going forward, the NRNB plans to further develop methods for differential and predictive network analysis, multi-scale network representation, and pathway analysis tools.
Computational Approaches to Systems BiologyMike Hucka
Presentation given at the Sydney Computational Biologists meetup on 21 August 2013 (http://australianbioinformatics.net/past-events/2013/8/21/computational-approaches-to-systems-biology.html).
National Resource for Networks Biology's TR&D Theme 3: Although networks have been very useful for representing molecular interactions and mechanisms, network diagrams do not visually resemble the contents of cells. Rather, the cell involves a multi-scale hierarchy of components – proteins are subunits of protein complexes which, in turn, are parts of pathways, biological processes, organelles, cells, tissues, and so on. In this technology research project, we will pursue methods that move Network Biology towards such hierarchical, multi-scale views of cell structure and function.
SciLifeLab is a Swedish national center for molecular biosciences that develops and provides advanced technologies for health and environmental research. It offers a cross-disciplinary research setting that interacts with healthcare, industry, and academia. SciLifeLab comprises multiple technology platforms across Swedish universities that provide services like genomics, proteomics, metabolomics, structural biology, chemical biology, imaging, and bioinformatics. It contributes to thousands of research projects annually and aims to advance life sciences research and applications.
The document provides an overview of the Center for Network Neuroscience (CNNS) at the University of North Texas. It summarizes the CNNS's research focus on studying nerve cell networks grown on microelectrode arrays, applications in pharmacology and toxicology, facilities and techniques. It highlights the need for private funding to expand research areas like disease models, develop high-throughput platforms, and eventually transition technologies to a small company.
This document provides an annual progress report for the National Resource for Network Biology (NRNB) for the period of May 1, 2011 to April 30, 2012. It summarizes the following:
1) Advances made in developing algorithms to identify network modules and use modules as biomarkers for disease. This includes methods to capture complex logical relationships within modules.
2) Progress on tools to enable new network analysis and visualization capabilities, including a new version of Cytoscape.
3) Growth of collaborations through the NRNB, which have nearly doubled over the past year to around 100 projects.
4) Continued development of the Cytoscape App Store to support the user and developer community.
Artista a network for ar tifical immune sys temsUltraUploader
This document introduces a proposed network called ARTIST (A Network for ARTifical Immune SysTems) that aims to foster collaboration among researchers in the field of artificial immune systems (AIS) in the UK. It provides brief biographies of the various researchers involved from different universities and backgrounds who are applying AIS concepts to areas like machine learning, security, and engineering. The network would provide infrastructure and funding to support knowledge sharing and joint projects. Its goals are to help establish the UK as a major player in AIS and facilitate technology transfer to industry.
Systems biology aims to understand biological systems as a whole rather than individual parts. Early criticisms saw molecular biology as too reductionist. Systems modeling using mathematical approaches also emerged. Standards like SBML and community-building efforts were important to allow sharing and integration of computational models between different research groups and software tools. This helped a systems biology community flourish by providing interoperability between various modeling approaches and data types.
This document describes a new type of heatmap called a "CoolMap" that allows for flexible multi-scale exploration of molecular network data. CoolMaps allow data to be collapsed and aggregated at different levels of a hierarchical tree, enabling visualization and pattern discovery across scales. This approach addresses limitations of conventional heatmaps and enables linking data to existing biological knowledge. Several case studies demonstrate how CoolMaps can provide new insights into gene expression, nutrition, DNA methylation, glucose monitoring, and network data. The core concepts and near-ready software releases are presented, along with acknowledgments.
Tijana Milenković is an assistant professor who develops algorithms for network alignment and mining of biological networks. Her lab has developed methods like GRAAL, H-GRAAL, and MAGNA for mapping similar nodes between networks to transfer knowledge across species. MAGNA directly optimizes edge conservation during alignment to improve accuracy. The lab has also applied network alignment to study aging networks and predict novel aging genes, and developed tools for dynamic network analysis and de-noising networks via link prediction.
Applied Bioinformatics & Chemoinformatics: Techniques, Tools, and OpportunitiesHezekiah Fatoki
The computational methods for in silico drug discovery have been broadly categories into two fields bioinformatics and chemoinformatics. In case of bioinformatics, major emphasis is on identification and validation of drug targets, mainly based on functional/structural annotation of genomes. In case of chemoinformatics or pharmacoinformatics, major emphasis is on designing of drug molecules or ligands and their interaction with drug targets.
The National Resource for Network Biology (NRNB) 2011 annual report highlights several accomplishments:
1) NRNB research studies identified correlated genotypes in friendship networks and genetic interactions underlying DNA damage responses.
2) Collaborations made progress mapping the yeast genetic interaction network and connecting networks to disease.
3) Cytoscape 3.0 development is on track to improve the software's modularity, APIs, and plugin architecture.
4) In its first year, NRNB established support services, research projects, and over 30 collaborations to advance network biology.
Automatically Generating Wikipedia Articles: A Structure-Aware ApproachGeorge Ang
The document describes an approach for automatically generating Wikipedia-style articles by using the structure of existing human-authored articles as templates. It involves inducing templates by analyzing section headings across documents, retrieving relevant excerpts from the internet for each template topic, and jointly training extractors to select excerpts that optimize both local relevance and global coherence across the entire article. The results confirm the benefits of incorporating structural information into the content selection process.
1) The document presents an approach called Multi-MMSB to identify context-dependent community structure across multiple networks.
2) Multi-MMSB jointly learns modules from all networks, allowing each module to be present in only a subset of networks.
3) When applied to synthetic and real biological data sets, Multi-MMSB identified context-specific modules more accurately than naive methods and discovered biologically plausible modules.
The document is a call for papers for the International Conference on Machine Learning and Data Analysis (ICMLDA 2008) to be held October 22-24, 2008 in San Francisco, USA. It provides information on submission guidelines and important dates, as well as an overview of topics that will be covered at the conference related to machine learning and data analysis techniques and applications. Accepted papers will be published in the conference proceedings and considered for publication in relevant journals.
This document discusses computational tools for analyzing biological networks and molecules. It begins by describing how networks can be inferred from molecular data using the Cytoscape platform and its apps. It then discusses how networks can help identify proteins of interest by describing a case study where network analysis identified additional proteins that provided an "explanation" for differences observed in proteomic data. The document concludes by discussing how computational analysis can help address questions that go beyond simply identifying the best network, such as which parts of a network are well-supported or could be modified to better fit the data.
The document outlines the complex network of molecular interactions involved in the cellular response to shear stress. It shows calcium channels activating phospholipase C which generates diacylglycerol and IP3, mobilizing calcium and activating protein kinase C. Protein kinase C phosphorylates targets and is inhibited by other kinases and phosphatases. Calcium activates calpain protease which degrades talin and paxillin, affecting focal adhesion complexes and mechanotransduction.
Summary: ENViz performs enrichment analysis for pathways and gene ontology (GO) terms in matched datasets of multiple data types (e.g. gene expression and metabolites or miRNA), then visualizes results as a Cytoscape network that can be navigated to show data overlaid on pathways and GO DAGs.
Background: Modern genomic, metabolomics, and proteomic assays produce multiplexed measurements that characterize molecular composition and biological activity from complimentary angles. Integrative analysis of such measurements remains a challenge to life science and biomedical researchers. We present an enrichment network approach to jointly analyzing two types of sample matched datasets and systematic annotations, implemented as a plugin to the Cytoscape [1] network biology software platform.
Approach: ENViz analyses a primary dataset (e.g. gene expression) with respect to a ‘pivot’ dataset (e.g. miRNA expression, metabolomics or proteomics measurements) and primary data annotation (e.g. pathway or GO). For each pivot entity, we rank elements of the primary data based on the correlation to the pivot across all samples, and compute statistical enrichment of annotation sets in the top of this ranked list based on minimum hypergeometric statistics [2]. Significant results are represented as an enrichment network - a bipartite graph with nodes corresponding to pivot and annotation entities, and edges corresponding to pivot-annotation pairs with statistical enrichmentscores above the user defined threshold. Correlations of primary data and pivot data are visually overlaid on biological pathways for significant pivot-annotation pairs using the WikiPathways resource [3], and on gene ontology terms. Edges of the enrichment network may point to functionally relevant mechanisms. In [4], a significant association between miR-19a and the cell-cycle module was substantiated as an association to proliferation, validated using a high-throughput transfection assay. The figures below show a pathway enrichment network, with pathway nodes green and miRNAs gray (left), network view of the edge between Inflammatory Response Pathway and mir-337-5p (center), and GO enrichment network with red areas indicating high enrichment for immune response and metabolic processes (right).
This document summarizes the accomplishments of the National Resource for Network Biology over a reporting period. It lists numerous quantitative metrics of success, including over 100 publications citing their grants, thousands of daily downloads and uses of their software tools, and training over 100 users. It also provides details on improvements and developments made to several of their modeling frameworks, algorithms, and software applications. Finally, it outlines the formation of a new working group on single-cell RNA-seq analysis and visualization, and improvements made to their computing infrastructure.
New Research Articles 2019 October Issue Signal & Image Processing An Interna...sipij
Signal & Image Processing: An International Journal (SIPIJ)
ISSN: 0976 – 710X [Online]; 2229 - 3922 [Print]
http://www.airccse.org/journal/sipij/index.html
Current Issue; October 2019, Volume 10, Number 5
Free- Reference Image Quality Assessment Framework Using Metrics Fusion and Dimensionality Reduction
Besma Sadou1, Atidel Lahoulou2, Toufik Bouden1, Anderson R. Avila3, Tiago H. Falk3 and Zahid Akhtar4, 1Non Destructive Testing Laboratory, University of Jijel, Algeria, 2LAOTI laboratory, University of Jijel, Algeria, 3University of Québec, Canada and 4University of Memphis, USA
Test-cost-sensitive Convolutional Neural Networks with Expert Branches
Mahdi Naghibi1, Reza Anvari1, Ali Forghani1 and Behrouz Minaei2, 1Malek-Ashtar University of Technology, Iran and 2Iran University of Science and Technology, Iran
Robust Image Watermarking Method using Wavelet Transform
Omar Adwan, The University of Jordan, Jordan
Improvements of the Analysis of Human Activity Using Acceleration Record of Electrocardiographs
Itaru Kaneko1, Yutaka Yoshida2 and Emi Yuda3, 1&2Nagoya City University, Japan and 3Tohoku University, Japan
http://www.airccse.org/journal/sipij/vol10.html
The National Resource for Network Biology (NRNB) aims to advance network biology science through bioinformatic methods, software, infrastructure, collaboration, and training. In the past year, the NRNB made progress in its specific aims, including developing new network analysis methods, catalyzing changes in network representation, establishing software and databases, engaging in collaborations, and providing training opportunities. Going forward, the NRNB plans to further develop methods for differential and predictive network analysis, multi-scale network representation, and pathway analysis tools.
Computational Approaches to Systems BiologyMike Hucka
Presentation given at the Sydney Computational Biologists meetup on 21 August 2013 (http://australianbioinformatics.net/past-events/2013/8/21/computational-approaches-to-systems-biology.html).
National Resource for Networks Biology's TR&D Theme 3: Although networks have been very useful for representing molecular interactions and mechanisms, network diagrams do not visually resemble the contents of cells. Rather, the cell involves a multi-scale hierarchy of components – proteins are subunits of protein complexes which, in turn, are parts of pathways, biological processes, organelles, cells, tissues, and so on. In this technology research project, we will pursue methods that move Network Biology towards such hierarchical, multi-scale views of cell structure and function.
SciLifeLab is a Swedish national center for molecular biosciences that develops and provides advanced technologies for health and environmental research. It offers a cross-disciplinary research setting that interacts with healthcare, industry, and academia. SciLifeLab comprises multiple technology platforms across Swedish universities that provide services like genomics, proteomics, metabolomics, structural biology, chemical biology, imaging, and bioinformatics. It contributes to thousands of research projects annually and aims to advance life sciences research and applications.
The document provides an overview of the Center for Network Neuroscience (CNNS) at the University of North Texas. It summarizes the CNNS's research focus on studying nerve cell networks grown on microelectrode arrays, applications in pharmacology and toxicology, facilities and techniques. It highlights the need for private funding to expand research areas like disease models, develop high-throughput platforms, and eventually transition technologies to a small company.
This document provides an annual progress report for the National Resource for Network Biology (NRNB) for the period of May 1, 2011 to April 30, 2012. It summarizes the following:
1) Advances made in developing algorithms to identify network modules and use modules as biomarkers for disease. This includes methods to capture complex logical relationships within modules.
2) Progress on tools to enable new network analysis and visualization capabilities, including a new version of Cytoscape.
3) Growth of collaborations through the NRNB, which have nearly doubled over the past year to around 100 projects.
4) Continued development of the Cytoscape App Store to support the user and developer community.
Artista a network for ar tifical immune sys temsUltraUploader
This document introduces a proposed network called ARTIST (A Network for ARTifical Immune SysTems) that aims to foster collaboration among researchers in the field of artificial immune systems (AIS) in the UK. It provides brief biographies of the various researchers involved from different universities and backgrounds who are applying AIS concepts to areas like machine learning, security, and engineering. The network would provide infrastructure and funding to support knowledge sharing and joint projects. Its goals are to help establish the UK as a major player in AIS and facilitate technology transfer to industry.
Systems biology aims to understand biological systems as a whole rather than individual parts. Early criticisms saw molecular biology as too reductionist. Systems modeling using mathematical approaches also emerged. Standards like SBML and community-building efforts were important to allow sharing and integration of computational models between different research groups and software tools. This helped a systems biology community flourish by providing interoperability between various modeling approaches and data types.
This document describes a new type of heatmap called a "CoolMap" that allows for flexible multi-scale exploration of molecular network data. CoolMaps allow data to be collapsed and aggregated at different levels of a hierarchical tree, enabling visualization and pattern discovery across scales. This approach addresses limitations of conventional heatmaps and enables linking data to existing biological knowledge. Several case studies demonstrate how CoolMaps can provide new insights into gene expression, nutrition, DNA methylation, glucose monitoring, and network data. The core concepts and near-ready software releases are presented, along with acknowledgments.
Tijana Milenković is an assistant professor who develops algorithms for network alignment and mining of biological networks. Her lab has developed methods like GRAAL, H-GRAAL, and MAGNA for mapping similar nodes between networks to transfer knowledge across species. MAGNA directly optimizes edge conservation during alignment to improve accuracy. The lab has also applied network alignment to study aging networks and predict novel aging genes, and developed tools for dynamic network analysis and de-noising networks via link prediction.
Applied Bioinformatics & Chemoinformatics: Techniques, Tools, and OpportunitiesHezekiah Fatoki
The computational methods for in silico drug discovery have been broadly categories into two fields bioinformatics and chemoinformatics. In case of bioinformatics, major emphasis is on identification and validation of drug targets, mainly based on functional/structural annotation of genomes. In case of chemoinformatics or pharmacoinformatics, major emphasis is on designing of drug molecules or ligands and their interaction with drug targets.
The National Resource for Network Biology (NRNB) 2011 annual report highlights several accomplishments:
1) NRNB research studies identified correlated genotypes in friendship networks and genetic interactions underlying DNA damage responses.
2) Collaborations made progress mapping the yeast genetic interaction network and connecting networks to disease.
3) Cytoscape 3.0 development is on track to improve the software's modularity, APIs, and plugin architecture.
4) In its first year, NRNB established support services, research projects, and over 30 collaborations to advance network biology.
Automatically Generating Wikipedia Articles: A Structure-Aware ApproachGeorge Ang
The document describes an approach for automatically generating Wikipedia-style articles by using the structure of existing human-authored articles as templates. It involves inducing templates by analyzing section headings across documents, retrieving relevant excerpts from the internet for each template topic, and jointly training extractors to select excerpts that optimize both local relevance and global coherence across the entire article. The results confirm the benefits of incorporating structural information into the content selection process.
1) The document presents an approach called Multi-MMSB to identify context-dependent community structure across multiple networks.
2) Multi-MMSB jointly learns modules from all networks, allowing each module to be present in only a subset of networks.
3) When applied to synthetic and real biological data sets, Multi-MMSB identified context-specific modules more accurately than naive methods and discovered biologically plausible modules.
The document is a call for papers for the International Conference on Machine Learning and Data Analysis (ICMLDA 2008) to be held October 22-24, 2008 in San Francisco, USA. It provides information on submission guidelines and important dates, as well as an overview of topics that will be covered at the conference related to machine learning and data analysis techniques and applications. Accepted papers will be published in the conference proceedings and considered for publication in relevant journals.
This document discusses computational tools for analyzing biological networks and molecules. It begins by describing how networks can be inferred from molecular data using the Cytoscape platform and its apps. It then discusses how networks can help identify proteins of interest by describing a case study where network analysis identified additional proteins that provided an "explanation" for differences observed in proteomic data. The document concludes by discussing how computational analysis can help address questions that go beyond simply identifying the best network, such as which parts of a network are well-supported or could be modified to better fit the data.
The document outlines the complex network of molecular interactions involved in the cellular response to shear stress. It shows calcium channels activating phospholipase C which generates diacylglycerol and IP3, mobilizing calcium and activating protein kinase C. Protein kinase C phosphorylates targets and is inhibited by other kinases and phosphatases. Calcium activates calpain protease which degrades talin and paxillin, affecting focal adhesion complexes and mechanotransduction.
Atherosclerosis is the buildup of plaque in artery walls due to injuries to the endothelial lining. Endothelial cells regulate blood flow through signal transduction and are sensitive to physical forces like shear stress from fluid movement. We developed a systems biology model with over 700 molecular reactions to investigate and predict how endothelial cells respond to shear stress at the molecular level. While our model matched some experimental data, it failed to capture other dynamics, suggesting there are still unknown interactions and responses that need to be incorporated.
1. The document discusses systems biology approaches to modeling the endothelial cell response to fluid shear stress. It describes experimental techniques to apply controlled fluid shear stress and measure downstream cellular responses.
2. Mathematical models are formulated to represent molecular interactions and pathways involved in the shear stress response. Model predictions are compared to experimental data to validate and refine the models.
3. The models can provide insights into critical components, feedback loops, and how external perturbations may influence the cellular response to shear stress. Further experimental validation of model predictions is needed.
This thesis examines how endothelial cells respond and polarize in response to fluid flow through three-dimensional modelling and simulations. It first reviews prior models of cytoskeleton dynamics and mechanics. It then presents a Brownian dynamics model of actin polymerization during lamellipodium formation. A boundary integral representation is used to model fluid flow over a single cell. A Kelvin-body model couples this flow to a PDE model of Rho GTPase activity to understand mechanotransduction and signal transduction dynamics during endothelial cell polarization and elongation in response to fluid flow. Validation against experimental data on Rho GTPase activation time courses is also discussed.
This PhD thesis presents a mathematical model of the signalling pathways regulating endothelial cell responses to fluid shear stress. The model consists of 8 interconnected modules describing processes such as calcium dynamics, calpain activity, integrin activation, and the phosphorylation of proteins like FAK and Src. Differential equations are used to simulate the dynamic behaviour of molecular species over time under varying shear stress conditions. The model aims to improve understanding of how fluid flow stimulation is converted into biochemical signals in endothelial cells and how this relates to the development of atherosclerosis.
This document is a resume for Gautam Machiraju. It summarizes his education and research experience. He has a B.A. in Applied Mathematics from UC Berkeley with a concentration in Mathematical Biology and a minor in Bioengineering. He has worked on several research projects involving mathematical modeling and data analysis related to biology and healthcare. These include modeling cancer biomarker shedding kinetics, mining literature for biomarker data, and using deep learning on patient time-series data. He has strong skills in programming, mathematics, and bioinformatics.
This document is a resume for Gautam Machiraju. It summarizes his education and research experience. He has a B.A. in Applied Mathematics from UC Berkeley with a concentration in Mathematical Biology and a minor in Bioengineering. He has worked on several research projects involving mathematical modeling and data analysis related to cancer biomarkers, genomics, and proteomics. His skills include programming, mathematics, data science, and laboratory techniques. He is currently a bioinformatics research assistant at Stanford University School of Medicine.
The document discusses the development of a Laboratory Assistant Suite (LAS) database application to manage data from a preclinical cancer model experiment involving implanting patient tumor samples in mice. It provides background on using such preclinical models for personalized cancer medicine. It describes the contributions of two research institutions (IRCC and Politecnico di Torino) to the LAS project and outlines the data flow, requirements, and database design for the LAS application.
Thomas Charles Ferree has over 25 years of experience in signal processing, algorithm development, and neuroscience research. He has a PhD in Physics from the University of Colorado and has held positions at several universities and research institutions. His research has focused on developing algorithms and models for analyzing EEG, EIT, and other biological signal data to study visual attention, stroke detection, and the neurological effects of various stimuli. He has extensive experience developing software and analyzing data across various computing platforms.
This document discusses data management and curation in bioinformatics. It describes Susanna-Assunta Sansone as the principal investigator and team leader at the University of Oxford e-Research Centre, where her team works on data management, biocuration, software development, databases, and community standards and ontologies for various domains including toxicology, health, and agriculture. The document promotes the importance of data standards to enable data sharing and reproducibility in bioscience research.
EiTESAL eHealth Conference 14&15 May 2017 EITESANGO
This document discusses bioinformatics and some of its key concepts and tools. It begins with definitions of bioinformatics as the intersection of biology, computer science, and information technology. It then discusses some of the data formats, tools, and skills used in bioinformatics, including working with nucleotide sequence data, translating sequences into amino acids, and analyzing large datasets. It also summarizes how ontologies are used to represent concepts and how various data types are organized and stored in databases for analysis.
SooryaKiran Bioinformatics is a global bioinformatics solutions provider that focuses on customized bioinformatics services and products. It develops algorithms and software for biological sequence analysis, structure prediction, and other areas. Key products include tools for sequence generation, analysis, and homology identification. The company collaborates with research institutions and has provided solutions for SNP analysis, genome analysis, and mitochondrial DNA analysis to clients around the world.
In this deck from the 2014 HPC User Forum in Seattle, Jack Collins from the National Cancer Institute presents: Genomes to Structures to Function: The Role of HPC.
Watch the video presentation: http://wp.me/p3RLHQ-d28
OVium Bio-Information Solutions use forefront algorithms to analyze key data resources such NCBI, EBLM and PDB to develop cell signal pathways.
OVium employs cloud and MPP computing solutions with homology and signal network mapping to develop chemical and protein pathways for discovery research.
Semantic Web & Web 3.0 empowering real world outcomes in biomedical research ...Amit Sheth
Talk presented in Spain (WiMS 2013/UAM-Madrid, UMA-Malaga), June 2013.
Replaces earlier version at: http://www.slideshare.net/apsheth/semantic-technology-empowering-real-world-outcomes-in-biomedical-research-and-clinical-practices
Biomedical and translational research as well as clinical practice are increasingly data driven. Activities routinely involve large number of devices, data and people, resulting in the challenges associated with volume, velocity (change), variety (heterogeneity) and veracity (provenance, quality). Equally important is to realize the challenge of serving the needs of broader ecosystems of people and organizations, extending traditional stakeholders like drug makers, clinicians and policy makers, to increasingly technology savvy and information empowered patients. We believe that semantics is becoming centerpiece of informatics solutions that convert data into meaningful, contextually relevant information and insights that lead to optimal decisions for translational research and 360 degree health, fitness and well-being.
In this talk, I will provide a series of snapshots of efforts in which semantic approach and technology is the key enabler. I will emphasize real-world and in-use projects, technologies and systems, involving significant collaborations between my team and biomedical researchers or practicing clinicians. Examples include:
• Active Semantic Electronic Medical Record
• Semantics and Services enabled Problem Solving Environment for T.cruzi (SPSE)
• Data Mining of Cardiology data
• Semantic Search, Browsing and Literature Based Discovery
• PREscription Drug abuse Online Surveillance and Epidemiology (PREDOSE)
• kHealth: development of a knowledge-enhanced sensing and mobile computing applications (using low cost sensors and smartphone), along with ability to convert low level observations into clinically relevant abstractions
Further details are at http://knoesis.org/amit/hcls
Alena Simalatsar is a seasoned R&D engineer with over 5 years of experience in biomedical engineering and medical devices. She has led complex multidisciplinary projects from research to prototyping, including developing intelligent algorithms for anesthesia delivery and a portable ultrasound system. She is skilled in conducting experiments, identifying issues, and determining solutions. She is looking for new challenges to develop innovative healthcare products using her expertise in areas such as R&D, product design, algorithms, testing, and project management.
In this deck from the HPC User Forum, Rick Stevens from Argonne presents: AI for Science.
"Artificial Intelligence (AI) is making strides in transforming how we live. From the tech industry embracing AI as the most important technology for the 21st century to governments around the world growing efforts in AI, initiatives are rapidly emerging in the space. In sync with these emerging initiatives including U.S. Department of Energy efforts, Argonne has launched an “AI for Science” initiative aimed at accelerating the development and adoption of AI approaches in scientific and engineering domains with the goal to accelerate research and development breakthroughs in energy, basic science, medicine, and national security, especially where we have significant volumes of data and relatively less developed theory. AI methods allow us to discover patterns in data that can lead to experimental hypotheses and thus link data driven methods to new experiments and new understanding."
Watch the video: https://wp.me/p3RLHQ-kQi
Learn more: https://www.anl.gov/topic/science-technology/artificial-intelligence
and
http://hpcuserforum.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Dr. Justin Feigelman has extensive experience in modeling, simulation, and high performance computing applied to biological systems. He holds a Ph.D. in mathematical modeling of biological systems from the Technical University of Munich and has worked as a postdoctoral researcher at ETH Zurich and the Helmholtz Zentrum München. He is skilled in C++, Matlab, R, Python, and Bayesian methods and has applied these skills to problems in molecular biology, healthcare, and life sciences.
Sci Know Mine 2013: What can we learn from topic modeling on 350M academic do...William Gunn
This document discusses topic modeling on 350 million documents from Mendeley. It describes how topic modeling can be used to categorize documents into topics and subcategories, though categorization is imperfect and topics change over time. It also discusses how topic modeling and metrics can help with fact discovery and reproducibility of research to build more robust datasets.
The Taverna Workflow Management Software Suite - Past, Present, FuturemyGrid team
The document summarizes the Taverna workflow management software. It discusses how Taverna allows users to visually design and execute workflows to analyze data through web services, scripts, and other tools. The summary highlights that Taverna uses a dataflow model and supports mixing different step types, nested workflows, and interactions. It also discusses how Taverna aims to advance scientific discovery by making workflows reusable, adaptive to different infrastructures, and able to process data at large scales.
KnetMiner, with a silent "K" and standing for Knowledge Network Miner, is a suite of open-source software tools developed at Rothamsted Research for integrating and visualising large biological datasets in order to accelerate gene discovery. The software mines the myriad databases that describe an organism’s biology to present links between relevant pieces of information, such as genes, biological pathways, phenotypes or publications. The aim is to provide leads for scientists who are investigating the molecular basis for a particular trait or ways of improving the organism’s performance in some way
From data to knowledge – the Ondex System for integrating Life Sciences data ...Catherine Canevet
The ONDEX system is an open source data integration platform developed under the SABR initiative from 2008-2011 to create a robust, extensible system for supporting systems biology research. ONDEX stores data as a graph of concepts and relations, imports data through parsers, maps concepts to create relations, and performs consistency checks. It was used in four demonstrator projects involving three research centers to integrate life sciences data sources, including identifying new targets for improving bioenergy crops.
[DSC Europe 23][DigiHealth] Vesna Pajic - Machine Learning Techniques for omi...DataScienceConferenc1
The document discusses machine learning techniques for analyzing omics data. It introduces Velsera, a bioinformatics company, and describes how they used machine learning to predict cancer cell line responses to drugs based on gene expression data. Specifically, they cleaned the data, performed feature selection, and tested models like elastic net, GAMs, and XGBoost (which performed best). The final model identified 20 important genes, including one the client was interested in and another potential biomarker the client was unaware of.
Continuous modeling - automating model building on high-performance e-Infrast...Ola Spjuth
This document discusses continuous modeling and automating model building on high-performance infrastructures. It notes the new challenges of data management, analysis, scaling, and automation posed by high-throughput technologies. The author's research focuses on enabling high-throughput biology through large-scale predictive modeling, evaluating performance, and automating model re-building. Predictive toxicology and pharmacology are becoming data-intensive due to more data sources. The document explores modeling large datasets on high-performance computing infrastructures and whether workflow systems or cloud/Big Data frameworks could improve modeling.
Continuous modeling - automating model building on high-performance e-Infrast...
DR KL CV v5
1. PETROU SAXINH 5, SKYDRA PELLAS, 58500, GREECE
P H ONE 2381082176 • M OBI LE 6972591011• E -MAIL LYKOSTRATIS@GOOGLEMAI L. COM
KONSTANTINOS LYKOSTRATIS
PROFILE
• An experienced project developer and process manager with an exemplary academic background and a talent
for high-technology enterprise endeavors;
• A clear and methodical thinker, with a considerable passion for robust design, able to balance short-term
pressures with long-terms objectives;
• A resourceful multi-tasker, enthusiastic communicator and rapid learner who thrives as an individual but excels
in a team environment;
• A veritable jack-of-all-trades and master of several.
PROFFESIONAL EXPERIENCE
2009 – 2010 IstosOnline Advanced Internet Solutions - Project Manager
• Established strategy, Co-ordinated the early stages of company development (www.istosonline.gr) and the
engineering of open source social-learning software solutions for use in academic institutions (Campus
Information Management Framework as an academic collaboration and e-learning environment);
• Designed a series of company Standard Operating Procedures for Software Development and Revision
Control, Software Configuration Management and Change Request Management based upon the Rapid
Application Development Process for software prototyping;
• Designed the company Policy and Standards Framework for Customer Support Management and Service
Level Agreements, Quality Management/Audit Systems and Training Delivery Programs, Customer
Engagement and Pricing Models of software deployment over the internet;
• Chief Architect of the ErgosShop Software as a Service E-Commerce model (www.ergosshop.gr)
2008 – 2009 Military Service – Greek Airforce - Airman, IT Systems
• Served 1 year as Airman in the 113 Battle Wing, Commander’s Detail, Thessaloniki International Airport
“Macedonia”, including 1-month secondment to the island of Lemnos, Battle wing 130, Lemnos Airport
2002-2007 Ludwig Institute for Cancer Research - Systems Biology Researcher
• Privately contracted by the Ludwig Institute for Cancer Research, London, for Research on Systems Biology
and the development of several modelling methodology protocols under Prof A.J. Ridley and Prof M. Zvelebil.
Summer 2002 Astra Zeneca R&D - Bioinformatics Researcher
• Worked under Dr. Carl-Morton Firth developing Bioinformatics tools and designing an Automated SNP
Analyser utilizing MySQL, Perl, CGI, web-design, Java, and data processing.
Spring 2001 Leicester Royal Infirmary - Mol. Genetics Researcher
• Worked under Prof J.R Pringley (University Hospital of Leicester) in developing experimental protocols
for the design & development of the lab technique: DNA bisulfite treatment & Methylation Specific PCR.
2. EDUCATION AND QUALIFICATIONS
2002-2007 PhD, Computational Systems Biology & Bioinformatics
University College London
Primary areas of interdisciplinary research:
• Mathematical modelling and Computational Biology
• Applied Biophysics in Biological Systems
• Chemical Engineering Principles of Fluids in Biological Systems
• Biochemistry/Molecular Genetics in Complex Biological Models
• Molecular and Cellular Systems Biology
• Algorithms for model parameter optimization and sensitivity analysis
• Software/Database development
Additional attributes and skills developed:
• Strong oral/presentation and written communication skills
• Time management: Organisation, planning, prioritisation skills
• Initiative, and ability to work independently
• Problem solving, resourcefulness
• Analytical thinking, systems theory
• Graduate course assistant trainer, multidisciplinary science communication.
2001 -2002 MRes, Bioinformatics (Awarded Distinction)
University of York
• Biological
sequence analysis
• Programming in Perl • Machine learning
• Programming in JAVA • Pattern
recognition and Neural Networks
• Databases (SQL) • Molecular aspects of disease
• Advanced Algebra & Calculus • Structural Bioinformatics
• Transferable/Communication skills • Statistics (Multivariate Data Analysis)
1998- 2001 BSc, Human Genetics (Award for Excellence in Research)
University of Leicester
2:1 Honours degree.
1998 Panhellenics (Greek A-Levels)
Skydra Senior College
Biology (85%), Chemistry (80%), Physics (70%), Mathematics (70%), Greek Literature (66.5%)
3. INVITED ORAL PRESENTATIONS
October 2007 - Rome, Italy
• Seminar title: Mathematical Modelling of the shear stress response in endothelial cells. Speaker: Dr Kostas
Lykostratis. Held at Mouse Biology Unit, EMBL Montetorondo (Rome, Italy) by Dr Liliana Minichiello.
March 2005 - London, UK
• Seminar title: Decoding the dynamics of molecular interactions. Speaker: Kostas Lykostratis. Held at the
Institute of Structural and Molecular Biology, School of Crystallography, Birckbeck College, London.
May 2005 - Zurich, Switzerland
• Seminar title: Systems Biology of the Shear Stress Response. Speaker: Kostas Lykostratis. Meeting held by the
Cell Migration Consortium, held at Switzerland: Lake Thun
RESEARCH EXPERIENCE
Sep-June Ludwig Institute for Cancer Research, University College London (UCL PhD research)
2002-2007 Mathematical modelling of shear stress signaling in endothelial cells.
In this project, a modelling and Systems Biology approach was taken to investigate
and understand better the endothelial signal transduction networks that convert fluid flow
stimulation into biochemical signals. A signal transduction network was built from integrin
cell surface receptors to activation of the tyrosine kinases FAK and Src. To model how fluid
flow initiates signalling in this network, the shear stress-induced calcium influx and the
viscoelastic response of transmembrane receptors such as integrins to mechanical force were
examined by means of mathematical modelling, using ordinary differential equations. These
effects were used as primary activators of the shear stress response in endothelial cells,
allowing quantitative analysis of the intracellular signal transduction flow which propagates
from integrin to paxillin, FAK and Src activation. The magnitude and dependencies of each
influence were examined individually and in conjunction with each other. The model was
used to investigate the role and dynamic regulation of previously unstudied molecules in the
network and the simulated results were compared against experimental data.
Techniques: Systems Biology software; Data analysis, Modelling & simulations, Database design
2003 -2004 Bogue Fellowship, Research training on Systems Biology, USA, Seattle
Institute for Systems Biology, Seattle, Washington State, USA
The fellowship allowed for a six-month external placement research training scheme to take
place in Seattle, USA, at the institute for Systems Biology under the presidency of Dr Leroy
Hood. Various short-term projects involved training and applications on:
• Multivariate Statistical Analysis of Biological Data
• Mathematical Modelling Design and Systems Biology
• Matlab simulations on ODE/PDE systems
• Applied principles of Multidisciplinary research: Combinatorial applications of Biophysics,
Chemical Engineering. Molecular Biology, Genetics, Mathematics, Computer Science
• Interdisciplinary Communication Skills
4. May-Sep Astra Zeneca R&D (MRes External placement)
2002 Design of an Automated SNP Viewer – (Awarded Prize for Research Excellence)
The project involved the design of a system for automated mining of SNP-information
and the generation of new information previously unavailable. An interactive viewer was
developed to aid visualisation of the gene of interest, the coding region, the SNP
locations and the effects of the polymorphisms at the protein level. The developed
system was used as a template for the identification of SNPs that might play key role in
disease genes or genes with differential behaviour on drugs and medical treatments.
Techniques: Perl programming, JAVA programming, CGI scripting, HTML editing, SRS.
Feb-Apr MRes project II (University if York)
2002 Identification of conserved core structures and residues in kinesins
Structural alignments of all the kinesin motor domain structures confirmed that these
enzymes share a common core structure and revealed extensive similarity located outside
the conserved nucleotide binding site residues. A model structure composed of the
structurally invariant core residues was constructed. This model was subsequently used
to scan the entire RCSB PDB database for structurally similar proteins.
Techniques: Molecular structure analysis, multiple structural alignments and Perl scripting.
Nov-Dec MRes project I (University of York)
2001 Computational Analysis of Microarray Data
Techniques: Examination of gene expression levels of 1611 genes using Hierarchical
and K-means clustering, Principal Component Analsysis and Partial Least Squares.
Oct-Apr BSc project Thesis (University of Leicester)
2000-2001 Investigation into the methylation state of Caspase 8 and E-cadherin in cancer cell lines
and formalin fixed tissues from neuroblastomas.
June-Sept Research assistant (Leicester Royal Infirmary)
2000 Developing experimental protocols for the design and refinement of a new
experimental technique: DNA bisulfite treatment and Methylation Specific PCR.
TEACHING EXPERIENCE
• Structural Bioinformatics Graduate Course Seminars (2003, 2004). University College London - Birkbeck College.
• Several Computational Molecular Biology undergraduate laboratory practicals at University of York, 2002
• Several 1st and 2nd year Biological Sciences undergraduate laboratory practicals at Leicester University, 2001
AWARDS/DISTINCTIONS/ ACHIEVEMENTS
• UK National Young Bioinformatician of the Year 2004 Award (Co-Winner), Young
Bioinformaticians Forum, Oxford 2004, WELCOME TO THE FUTURE. DRUG DISCOVERY TODAY, VOLUME 10,
ISSUE 3, 1 FEBRUARY 2005, PAGES 172-173.
• Bogue Research Fellowship Awarded by Dean of Life Sciences of University College London, UCL 2004
• Award for Best Annual Poster Presentation, Biological Sciences Faculty, Computer Science Dept,
University of York 2002
• Award for Best Annual Poster Presentation, Biological Sciences Faculty, Genetics Dept, University of
Leicester 2001
5. • Undergraduate Research Excellence Award, Biological Sciences Faculty, Genetics Dept, University of
Leicester 2001
KEY TECHNICAL SKILLS
Computing Bioinformatics and Systems Biology Software:
• Systems Biology/Mathematicall Modelling: (MATLAB - Optimisation, PDE and Statistics
Toolboxes, Cytoscape, SBW, Jarnac, Jdesigner, GEPASI, Dizzy, Virtual-Cell, SBML, CellML) ,
• Molecular Modelling: (Quanta, Rasmol, VMD, SwissPDB);
• Structural/Sequence Alignments: (CAMINE, VAST, DALI, ClustalX, GCG, HMMer, Psi-Blast);
• Phylogenetic Analysis: (Treeview, PAUP),
• DNA Array Data: (EisenLab, Cluster-Treeview, Jexpress, TigrMEV, AMANDA, Genespring);
Other Software & Operating Systems:
MS Office, Mac iWork, Reference Manager; Corel Draw, Adobe Illustrator and
Photoshop, Windows, Mac OS X and Unix-Linux operating systems;
Development Programming Languages and Database Systems
Software Development Lifecycle (Rational Unified Process);
Object-Oriented Development and Design Patterns;
Java (3 years), Perl (3 years), Drupal (2 years); Matlab (4 years);
Currently studying Python as a Rapid Application Framework
Knowledge of Open Source/Free Software Licensing and Copyright Law.
Systems Administration
Relational Database Design
University level Mathematics.
Languages • Fluency in English
• Fluency in Greek
• Basic studies in French and Italian
Other • Clean driving license (automobile). Obtained June 1998.
• Self-taught guitar player and regularly composing own songs
• Avid interest in reading philosophy and biographical manuscripts