geWorkbench: an open source platform for integrative genomics.

Publication Date: 2010 May 28 PMID: 20511363
Authors: Floratos, A. – Smith, K. – Ji, Z. – Watkinson, J. – Califano, A.
Journal: Bioinformatics

SUMMARY: geWorkbench (genomics Workbench) is an open source Java desktop application that provides access to an integrated suite of tools for the analysis and visualization of data from a wide range of genomics domains (gene expression, sequence, protein structure, systems biology). More than 70 distinct plug-in modules are currently available implementing both classical analyses (several variants of clustering, classification, homology detection etc) as well as state of the art algorithms for the reverse engineering of regulatory networks and for protein structure prediction, among many others. geWorkbench leverages standards-based middleware technologies to provide seamless access to remote data, annotation and computational servers thus enabling researchers with limited local resources to benefit from available public infrastructure. AVAILABILITY: The project site (http://www.geworkbench.org) includes links to self extracting installers for most OS platforms as well as instructions for building the application from scratch using the source code (which is freely available from the project’s SVN repository). CONTACT: geWorkbench support is available through the end-user and developer forums of the caBIG((R)) Molecular Analysis Tools Knowledge Center, https://cabig-kc.nci.nih.gov/Molecular/forums/.

post to: CiteULike

View full post on Bioinformatics

Systems Biology for Signaling Networks

Product Description
System Biology encompasses the knowledge from diverse fields such as Molecular Biology, Immunology, Genetics, Computational Biology, Mathematical Biology, etc. not only to address key questions that are not answerable by individual fields alone, but also to help in our understanding of the complexities of biological systems. Whole genome expression studies have provided us the means of studying the expression of thousands of genes under a particular condition and this technique had been widely used to find out the role of key macromolecules that are involved in biological signaling pathways. However, making sense of the underlying complexity is only possible if we interconnect various signaling pathways into human and computer readable network maps. These maps can then be used to classify and study individual components involved in a particular phenomenon. Apart from transcriptomics, several individual gene studies have resulted in adding to our knowledge of key components that are involved in a signaling pathway. It therefore becomes imperative to take into account of these studies also, while constructing our network maps to highlight the interconnectedness of the entire signaling pathways and the role of that particular individual protein in the pathway. This collection of articles will contain a collection of pioneering work done by scientists working in regulatory signaling networks and the use of large scale gene expression and omics data. The distinctive features of this book would be: Act a single source of information to understand the various components of different signaling network (roadmap of biochemical pathways, the nature of a molecule of interest in a particular pathway, etc.), Serve as a platform to highlight the key findings in this highly volatile and evolving field, and Provide answers to various techniques both related to microarray and cell signaling to the readers.

Order from Amazon Today Systems Biology for Signaling Networks

Research in Computational Molecular Biology: 14th Annual International Conference, RECOMB 2010, Lisbon, Portugal, April 25-28, 2010, Proceedings

Product Description
This book constitutes the refereed proceedings of the 14th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2010, held in Lisbon, Portugal, in April 2010. The 36 revised full papers were carefully reviewed and selected from 176 submissions. The papers cover all areas of computational molecular biology such as molecular sequence analysis; recognition of genes and regulatory elements; molecular evolution; gene expression; biological networks; sequencing and genotyping technologies; genomics; population genetics; systems biology; imaging; computational proteomics; and molecular structural biology.

Order from Amazon Today Research in Computational Molecular Biology: 14th Annual International Conference, RECOMB 2010, Lisbon, Portugal, April 25-28, 2010, Proceedings

Inference of Combinatorial Boolean Rules of Synergistic Gene Sets from Cancer Microarray Datasets.

Publication Date: 2010 Apr 21 PMID: 20410052
Authors: Park, I. – Lee, K. H. – Lee, D.
Journal: Bioinformatics

MOTIVATION: Gene set analysis has become an important tool for the functional interpretation of high-throughput gene expression datasets. Moreover, pattern analyses based on inferred gene set activities of individual samples have shown the ability to identify more robust disease signatures than individual gene based pattern analyses. Although a number of approaches have been proposed for gene set based pattern analysis, the combinatorial influence of deregulated gene sets on disease phenotype classification has not been studied sufficiently. RESULTS: We propose a new approach to inferring combinatorial Boolean rules of gene sets for a better understanding of cancer transcriptome and cancer classification. To reduce the search space of the possible Boolean rules, we identify small groups of gene sets that synergistically contribute to the classification of samples into their corresponding phenotypic groups (such as normal and cancer). We then measure the significance of the candidate Boolean rules derived from each group of gene sets; the level of significance is based on the class entropy of the samples selected in accordance with the rules. By applying the present approach to publicly available prostate cancer datasets, we identified 72 significant Boolean rules. Finally, we discuss several identified Boolean rules, such as the rule of glutathione metabolism (down) and prostaglandin synthesis regulation (down), which are consistent with known prostate cancer biology. AVAILABILITY: Scripts written in Python and R are available at http://biosoft.kaist.ac.kr/~ihpark/. The refined gene sets and the full list of the identified Boolean rules are provided in the supplementary file. CONTACT: dhlee@biosoft.kaist.ac.kr.

post to: CiteULike

View full post on Bioinformatics

Medical Informatics: Concepts, Methodologies, Tools, and Applications

Product Description
Technological advances of the past two decades have profoundly reshaped and enhanced all aspects of medical research and practice. So important has technology become to the ability to continue to drive new medical advances, from basic biomedical research to applied clinical practice and healthcare delivery management, that the science of biomedical technology has become an important discipline in its own right, critical to the missions of a full range of organizations that comprise the medical industry.

Medical Informatics: Concepts, Methodologies, Tools, and Applications holds the most complete collection of cutting-edge medical IT research available in topics such as clinical knowledge management, medical informatics, mobile health and service delivery, and gene expression. This four-volume compilation provides researchers, academicians, and scholars in the field of medical information technology with more than 200 chapters by over 250 international experts in medical informatics. Medical Informatics: Concepts, Methodologies, Tools, and Applications is an essential reference publication for every library and medical institute striving to remain up-to-date with the latest techniques, approaches, and education in the medical IT field.

Order from Amazon Today Medical Informatics: Concepts, Methodologies, Tools, and Applications

Next Page »