This fully updated book collects numerous data mining techniques, reflecting the acceleration and diversity of the development of data-driven approaches to the life sciences. The first half of the volume examines genomics, particularly metagenomics and epigenomics, which promise to deepen our knowledge of genes and genomes, while the second half of the book emphasizes metabolism and the metabolome as well as relevant medicine-oriented subjects. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and expert implementation advice that is useful for getting optimal results. Authoritative and practical, Data Mining for Systems Biology: Methods and Protocols, Second Edition serves as an ideal resource for researchers of biology and relevant fields, such as medical, pharmaceutical, and agricultural sciences, as well as for the scientists and engineers who are working on developing data-driven techniques, such as databases, data sciences, data mining, visualization systems, and machine learning or artificial intelligence that now are central to the paradigm-altering discoveries being made with a higher frequency.

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 Biology (TM) 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.

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 Biology " 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."

This volume contains papers presented at the 18th International Conference on Genome Informatics (GIW 2007) held at the Biopolis, Singapore from December 3 to 5, 2007. The GIW Series provides an international forum for the presentation and discussion of original research papers on all aspects of bioinformatics, computational biology and systems biology. Its scope includes biological sequence analysis, protein folding prediction, gene regulatory network, clustering algorithms, comparative genomics, and text mining. Boasting a history of 18 years, GIW is likely the longest-running international bioinformatics conference.A total of 16 papers were selected for presentation at GIW 2007 and inclusion in this book. The notable authors include Ming Li (University of Waterloo, Canada), Minoru Kanehisa (Kyoto University, Japan), Vladimir Kuznetsov (Genome Institute of Singapore), Tao Jiang (UC Riverside, USA), Christos Ouzounis (European Bioinformatics Institute, UK), and Satoru Miyano (University of Tokyo, Japan). In addition, this book contains abstracts from the five invited speakers: Frank Eisenhaber (Bioinformatics Institute, Singapore), Sir David Lane (Institute of Molecular and Cell Biology, Singapore), Hanah Margalit (The Hebrew University of Jerusalem, Israel), Lawrence Stanton (Genome Institute of Singapore), and Michael Zhang (Cold Spring Harbor Laboratory, USA).