Data mining for systems biology : methods and protocols
著者
書誌事項
Data mining for systems biology : methods and protocols
（Methods in molecular biology / John M. Walker, series editor, 1807）（Springer protocols）
Humana Press, c2018
2nd ed
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
1. Identifying Bacterial Strains from Sequencing Data
Tommi Maklin, Jukka Corander, and Antti Honkela
2. MetaVW: Large-Scale Machine Learning for Metagenomics Sequence Classification
Kevin Vervier, Pierre Mahe, and Jean-Philippe Vert
3. Online Interactive Microbial Classification and Geospatial Distributional Analysis Using BioAtlas
Jesper Lund, Qihua Tan, and Jan Baumbach
4. Generative Models for Quantification of DNA Modifications
Tarmo AEijoe, Richard Bonneau, and Harri Lahdesmaki
5. DiMmer: Discovery of Differentially Methylated Regions in Epigenome-Wide Association Study (EWAS) Data
Tobias Frisch, Jonatan Gottcke, Richard Roettger, Qihua Tan, and Jan Baumbach
6. Implementing a Transcription Factor Interaction Prediction System Using the GenoMetric Query Language
Stefano Perna, Arif Canakoglu, Pietro Pinoli, Stefano Ceri, and Limsoon Wong
7. Multiple Testing Tool to Detect Combinatorial Effects in Biology
Aika Terada and Koji Tsuda
8. 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, and Hiroshi Mamitsuka
9. Computing and Visualizing Gene Function Similarity and Coherence with NaviGO
Ziyun Ding, Qing Wei, and Daisuke Kihara
10. Analyzing Glycan Binding Profiles Using Weighted Multiple Alignment of Trees
Kiyoko F. Aoki-Kinoshita
11. Analysis of Fluxomic Experiments with Principal Metabolic Flux Mode Analysis
Sahely Bhadra and Juho Rousu
12. Analyzing Tandem Mass Spectra Using the DRIP Toolkit: Training, Searching, and Post-Processing
John T. Halloran
13. Sparse Modeling to Analyze Drug-Target Interaction Networks
Yoshihiro Yamanishi
14. DrugE-Rank: Predicting Drug-Target Interactions by Learning to Rank
Jieyao Deng, Qingjun Yuan, Hiroshi Mamitsuka, and Shanfeng Zhu
15. MeSHLabeler and DeepMeSH: Recent Progress in Large-Scale MeSH Indexing
Shengwen Peng, Hiroshi Mamitsuka, and Shanfeng Zhu
16. Disease Gene Classification with Metagraph Representations
Sezin Kircali Ata, Yuan Fang, Min Wu, Xiao-Li Li, and Xiaokui Xiao
17. Inferring Antimicrobial Resistance from Pathogen Genomes in KEGG
Minoru Kanehisa
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