Data Mining for Systems Biology

1. Front Matter

   Pages i-xi

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2. Identifying Bacterial Strains from Sequencing Data

   • Tommi Mäklin, Jukka Corander, Antti Honkela

   Pages 1-7

3. MetaVW: Large-Scale Machine Learning for Metagenomics Sequence Classification

   • Kévin Vervier, Pierre Mahé, Jean-Philippe Vert

   Pages 9-20

4. Online Interactive Microbial Classification and Geospatial Distributional Analysis Using BioAtlas

   • Jesper Lund, Qihua Tan, Jan Baumbach

   Pages 21-35

5. Generative Models for Quantification of DNA Modifications

   • Tarmo Äijö, Richard Bonneau, Harri Lähdesmäki

   Pages 37-50

6. 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

7. 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

8. Multiple Testing Tool to Detect Combinatorial Effects in Biology

   • Aika Terada, Koji Tsuda

   Pages 83-94

9. 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

10. Computing and Visualizing Gene Function Similarity and Coherence with NaviGO

    • Ziyun Ding, Qing Wei, Daisuke Kihara

    Pages 113-130

11. Analyzing Glycan-Binding Profiles Using Weighted Multiple Alignment of Trees

    • Kiyoko F. Aoki-Kinoshita

    Pages 131-140

12. Analysis of Fluxomic Experiments with Principal Metabolic Flux Mode Analysis

    • Sahely Bhadra, Juho Rousu

    Pages 141-161

13. Analyzing Tandem Mass Spectra Using the DRIP Toolkit: Training, Searching, and Post-Processing

    • John T. Halloran

    Pages 163-180

14. Sparse Modeling to Analyze Drug–Target Interaction Networks

    • Yoshihiro Yamanishi

    Pages 181-193

15. DrugE-Rank: Predicting Drug-Target Interactions by Learning to Rank

    • Jieyao Deng, Qingjun Yuan, Hiroshi Mamitsuka, Shanfeng Zhu

    Pages 195-202

16. MeSHLabeler and DeepMeSH: Recent Progress in Large-Scale MeSH Indexing

    • Shengwen Peng, Hiroshi Mamitsuka, Shanfeng Zhu

    Pages 203-209

17. Disease Gene Classification with Metagraph Representations

    • Sezin Kircali Ata, Yuan Fang, Min Wu, Xiao-Li Li, Xiaokui Xiao

    Pages 211-224

18. Inferring Antimicrobial Resistance from Pathogen Genomes in KEGG

    • Minoru Kanehisa

    Pages 225-239

19. Back Matter

    Pages 241-243

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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.

• Metagenomics
• Epigenomics
• Metabolomics
• Data sciences
• Machine learning
• Pharmaceutical science
• Artificial intelligence

• Bioinformatics Center, Kyoto University, Uji, Japan

  Hiroshi Mamitsuka

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