Data Mining for Systems Biology

Description

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. Front Matter ....Pages i-xi Identifying Bacterial Strains from Sequencing Data (Tommi Mäklin, Jukka Corander, Antti Honkela)....Pages 1-7 MetaVW: Large-Scale Machine Learning for Metagenomics Sequence Classification (Kévin Vervier, Pierre Mahé, Jean-Philippe Vert)....Pages 9-20 Online Interactive Microbial Classification and Geospatial Distributional Analysis Using BioAtlas (Jesper Lund, Qihua Tan, Jan Baumbach)....Pages 21-35 Generative Models for Quantification of DNA Modifications (Tarmo Äijö, Richard Bonneau, Harri Lähdesmäki)....Pages 37-50 DiMmer: Discovery of Differentially Methylated Regions in Epigenome-Wide Association Study (EWAS) Data (Tobias Frisch, Jonatan Gøttcke, Richard Röttger, Qihua Tan, Jan Baumbach)....Pages 51-62 Implementing a Transcription Factor Interaction Prediction System Using the GenoMetric Query Language (Stefano Perna, Arif Canakoglu, Pietro Pinoli, Stefano Ceri, Limsoon Wong)....Pages 63-81 Multiple Testing Tool to Detect Combinatorial Effects in Biology (Aika Terada, Koji Tsuda)....Pages 83-94 SiBIC: A Tool for Generating a Network of Biclusters Captured by Maximal Frequent Itemset Mining (Kei-ichiro Takahashi, David A. duVerle, Sohiya Yotsukura, Ichigaku Takigawa, Hiroshi Mamitsuka)....Pages 95-111 Computing and Visualizing Gene Function Similarity and Coherence with NaviGO (Ziyun Ding, Qing Wei, Daisuke Kihara)....Pages 113-130 Analyzing Glycan-Binding Profiles Using Weighted Multiple Alignment of Trees (Kiyoko F. Aoki-Kinoshita)....Pages 131-140 Analysis of Fluxomic Experiments with Principal Metabolic Flux Mode Analysis (Sahely Bhadra, Juho Rousu)....Pages 141-161 Analyzing Tandem Mass Spectra Using the DRIP Toolkit: Training, Searching, and Post-Processing (John T. Halloran)....Pages 163-180 Sparse Modeling to Analyze Drug–Target Interaction Networks (Yoshihiro Yamanishi)....Pages 181-193 DrugE-Rank: Predicting Drug-Target Interactions by Learning to Rank (Jieyao Deng, Qingjun Yuan, Hiroshi Mamitsuka, Shanfeng Zhu)....Pages 195-202 MeSHLabeler and DeepMeSH: Recent Progress in Large-Scale MeSH Indexing (Shengwen Peng, Hiroshi Mamitsuka, Shanfeng Zhu)....Pages 203-209 Disease Gene Classification with Metagraph Representations (Sezin Kircali Ata, Yuan Fang, Min Wu, Xiao-Li Li, Xiaokui Xiao)....Pages 211-224 Inferring Antimicrobial Resistance from Pathogen Genomes in KEGG (Minoru Kanehisa)....Pages 225-239 Back Matter ....Pages 241-243