Data Mining for Systems Biology
Description
The post-genomic revolution is witnessing the generation of petabytes of data annually, with deep implications ranging across evolutionary theory, developmental biology, agriculture, and disease processes. Data Mining for Systems Biology: Methods and Protocols , surveys and demonstrates the science and technology of converting an unprecedented data deluge to new knowledge and biological insight. The volume is organized around two overlapping themes, network inference and functional inference. Written in the highly successful Methods in Molecular BiologyT series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible protocols, and key tips on troubleshooting and avoiding known pitfalls.
Authoritative and practical, Data Mining for Systems Biology: Methods and Protocols also seeks to aid researchers in the further development of databases, mining and visualization systems that are central to the paradigm altering discoveries being made with increasing frequency.
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Content
Dense Module Enumeration in Biological Networks.-Discovering Interacting Domains and Motifs in Protein-Protein Interactions.-Global Alignment of Protein-Protein Interaction Networks.-Structure Learning for Bayesian Networks as Models of Biological Networks.-Supervised Inference of Gene Regulatory Networks from Positive and Unlabeled Examples.-Mining Regulatory Network Connections by Ranking Transcription Factor Target Genes using Time Series Expression Data.-Identifying Pathways of Co-ordinated Gene Expression.-Mining Frequent Subtrees in Glycan Data using the RINGS Glycan Miner Tool.-Chemogenomic Approaches to Infer drug-target interaction networks.-Localization Prediction and Structure-based in Silico Analysis of Bacterial Proteins- with Emphasis on Outer Membrane Proteins.-Analysis Strategy of Protein-Protein Interaction Networks.-Data Mining in the MetaCyc Family of Pathway Databases.-Gene Set/Pathway Enrichment Analysis.-Construction of Functional Linkage Gene Networks by Data Integration.-Genome-Wide Association Studies.-Viral Genome Analysis and Knowledge Management.-Molecular Network Analysis of Diseases and Drugs in KEG.