Knowledge-Based Bioinformatics

Preface. List of Contributors. PART I FUNDAMENTALS. Section 1 Knowledge-Driven Approaches. 1 Knowledge-based bioinformatics (Eric KarlNeumann). 1.1 Introduction. 1.2 Formal reasoning for bioinformatics. 1.3 Knowledge representations. 1.4 Collecting explicit knowledge. 1.5 Representing common knowledge. 1.6 Capturing novel knowledge. 1.7 Knowledge discovery applications. 1.8 Semantic harmonization: the power and limitation ofontologies. 1.9 Text mining and extraction. 1.10 Gene expression. 1.11 Pathways and mechanistic knowledge. 1.12 Genotypes and phenotypes. 1.13 The Web's role in knowledge mining. 1.14 New frontiers. 1.15 References. 2 Knowledge-driven approaches to genome-scaleanalysis (Hannah Tipney and Lawrence Hunter). 2.1 Fundamentals. 2.2 Challenges in knowledge-driven approaches. 2.3 Current knowledge-based bioinformatics tools. 2.4 3R systems: reading, reasoning and reporting the way towardsbiomedical discovery. 2.5 The Hanalyzer: a proof of 3R concept. 2.6 Acknowledgements. 2.7 References. 3 Technologies and best practices for buildingbio-ontologies (Mikel Egana Aranguren, RobertStevens, Erick Antezana, Jesualdo Tomas Fernandez-Breis,Martin Kuiper, and Vladimir Mironov). 3.1 Introduction. 3.2 Knowledge representation languages and tools for buildingbio-ontologies. 3.3 Best practices for building bio-ontologies. 3.4 Conclusion. 3.5 Acknowledgements. 3.6 References. 4 Design, implementation and updating of knowledgebases (Sarah Hunter, Rolf Apweiler, and Maria JesusMartin). 4.1 Introduction. 4.2 Sources of data in bioinformatics knowledge bases. 4.3 Design of knowledge bases. 4.4 Implementation of knowledge bases. 4.5 Updating of knowledge bases. 4.6 Conclusions. 4.7 References. Section 2 Data-Analysis Approaches. 5 Classical statistical learning inbioinformatics (Mark Reimers). 5.1 Introduction. 5.2 Significance testing. 5.3 Exploratory analysis. 5.4 Classification and prediction. 5.5 References. 6 Bayesian methods in genomics and proteomicsstudies (Ning Sun and Hongyu Zhao). 6.1 Introduction. 6.2 Bayes theorem and some simple applications. 6.3 Inference of population structure from genetic markerdata. 6.4 Inference of protein binding motifs from sequence data. 6.5 Inference of transcriptional regulatory networks from jointanalysis of protein DNA binding data and gene expressiondata. 6.6 Inference of protein and domain interactions from yeasttwo-hybrid data. 6.7 Conclusions. 6.8 Acknowledgements. 6.9 References. 7 Automatic text analysis for bioinformatics knowledgediscovery (Dietrich Rebholz-Schuhmann and Jung-jaeKim). 7.1 Introduction. 7.2 Information needs for biomedical text mining. 7.3 Principles of text mining. 7.4 Development issues. 7.5 Success stories. 7.6 Conclusion. 7.7 References. PART II APPLICATIONS. Section 3 Gene and Protein Information. 8 Fundamentals of gene ontology functionalannotation (Varsha K. Khodiyar, Emily C. Dimmer,Rachael P. Huntley, and Ruth C. Lovering). 8.1 Introduction. 8.2 Gene Ontology (GO). 8.3 Comparative genomics and electronic protein annotation. 8.4 Community annotation. 8.5 Limitations. 8.6 Accessing GO annotations. 8.7 Conclusions. 8.8 References. 9 Methods for improving genomeannotation (Jonathan Mudge and JenniferHarrow). 9.1 The basis of gene annotation. 9.2 The impact of next generation sequencing on genomeannotation. 9.3 References. 10 Sequences from prokaryotic, eukaryotic, and viral genomesavailable clustered according to phylotype on a Self-OrganizingMap (Takashi Abe, Shigehiko Kanaya, and ToshimichiIkemura). 10.1 Introduction. 10.2 Batch-learning SOM (BLSOM) adapted for genomeinformatics. 10.3 Genome sequence analyses using BLSOM. 10.4 Conclusions and discussion. 10.5 References. Section 4 Biomolecular Relationships andMeta-Relationships. 11 Molecular network analysis andapplications (Minlu Zhang, Jingyuan Deng, Chunsheng V.Fang, Xiao Zhang, and Long Jason Lu). 11.1 Introduction. 11.2 Topology analysis and applications. 11.3 Network motif analysis. 11.4 Network modular analysis and applications. 11.5 Network comparison. 11.6 Network analysis software and tools. 11.7 Summary. 11.8 Acknowledgement. 11.9 References. 12 Biological pathway analysis: an overview of Reactome andother integrative pathway knowledge bases (Robin A.Haw, Marc E. Gillespie, and Michael A. Caudy). 12.1 Biological pathway analysis and pathway knowledgebases. 12.2 Overview of high-throughput data capture technologies anddata repositories. 12.3 Brief review of selected pathway knowledge bases. 12.4 How does information get into pathway knowledge bases? 12.5 Introduction to data exchange languages. 12.6 Visualization tools. 12.7 Use case: pathway analysis in Reactome using statisticalanalysis of high-throughput data sets. 12.8 Discussion: challenges and future directions of pathwayknowledge bases. 12.9 References. 13 Methods and challenges of identifying biomolecularrelationships and networks associated with complexdiseases/phenotypes, and their application to drugtreatments (Mie Rizig). 13.1 Complex traits: clinical phenomenology and molecularbackground. 13.2 Why it is challenging to infer relationships between genesand phenotypes in complex traits? 317 13.3 Bottom-up or top-down: which approach is more useful indelineating complex traits key drivers? 13.4 High-throughput technologies and their applications incomplex traits genetics. 13.5 Integrative systems biology: a comprehensive approach tomining high-throughput data. 13.6 Methods applying systems biology approach in theidentification of functional relationships from gene expressiondata. 13.7 Advantages of networks exploration in molecular biology anddrug discovery. 13.8 Practical examples of applying systems biology approachesand network exploration in the identification of functional modulesand disease-causing genes in complex phenotypes/diseases. 13.9 Challenges and future directions. 13.10 References. Trends and conclusion. Index.

Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.

• Biologia, ciências da vida
• Biotecnologia