Knowledge-based Bioinformatics ^ from Analysis to Interpretation. Edited

‘Knowledge-Based Bioinformatics – From analysis to interpretation’, edited by Gil Alterovitz and Marco Ramoni, is a collection (http://onlinelibrary.wiley .com/book/10.1002/9780470669716) of works by 35 authors arranged in 13 Chapters of 4 sections and 2 parts. This book includes diverse knowledgebased bioinformatics issues that arise from rapidly increasing data generated by progressive biological research and high-throughput experimental technologies. In fact, the development and practical use of biological knowledge base is a main issue in bioinformatics literature. This book covers a broad range of topics related to the types of knowledge required for bioinformatics and extensive examples of knowledge databases for understanding of knowledge-based bioinformatics, and knowledge-driven discovery and various data analysis methods for its applications, focusing on representation, interpretation, acquisition, development, integration, maintenance, relationships and networks of biological knowledge. The book is organized into the following four sections: ‘Knowledge-Driven Approaches’ (Section 1), ‘Data-Analysis Approaches’ (Section 2), ‘Gene and Protein information’ (Section 3) and ‘Biomolecular Relationships and Meta-relationships’ (Section 4). Sections 1 and 2 introduce ‘Fundamentals’ (Part I), and using this background information ‘Applications’ (Part II) are discussed in Sections 3 and 4. Each section consists of three or four chapters under the same specific topic. At the end of the book, trends on current bioinformatics and conclusions are briefly mentioned. Chapter 1 begins by providing a review of historical and present knowledge discovery in bioinformatics, ranging from definition for knowledge, to formal reasoning, to knowledge representations (KRs). The issues on common knowledge and the capture of novel knowledge are also mentioned. Using this initial background, key topics of knowledge discovery and data mining (KDD) and its application domains are presented from various perspectives such as ontology, text information extraction, gene expression analysis, pathway structure, relation mappings between genotypes and phenotypes, web’s role in knowledge mining, information aggregation and articulation for linked knowledge, and new requirements for the next-generation KDD. Chapter 2 mentions that the biological researches have moved from high-quality experimental data generation to data analysis, and molecules of interest have moved from single genes and their behaviors or functions to groups of interacting entities. According to this shift, the attendant challenges of knowledgedriven approaches in finding and interacting with biomedical knowledge, e.g. automatically parsing specific meaning and key terms from unstructured text, handling of implicit knowledge and visualizing highly complex data, are addressed. The chapter also describes current knowledge-based bioinformatics tools: (i) enrichment tools such as GO (Gene Ontology), DAVID (Database for Annotation, Visualization and Integrated Discovery) and GSEA (Gene Set Enrichment Analysis) databases for interpreting groups of genes, (ii) network/interaction tools such as DIP (Database of Interacting Proteins) and (iii) much richer network tools such as STRING (Search Tool for the Retrieval of Interacting Genes/Protein) databases for supporting indirect interactions and comprehensive investigation of a group of interesting proteins and their associations with other biological entities. In addition, 3R knowledge-based systems built on three broad classes of methods: reading, reasoning and reporting and the Hanalyzer 3R system architecture are stated. Chapter 3 deals with ontology, one of major biological knowledge that is a computational formalization to provide consistent descriptions and reasoning schemes for entities in a specific domain and their relations by using controlled vocabularies or precise semantics. The authors points out that ontology building is in a transition stage from a craft to a fully industrial engineering discipline, and current bioontologies often reveal limitations in supporting automated reasoning. This chapter examines KR languages for building bio-ontologies such as the Resource Description Framework (RDF), the Web Ontology Language (OWL) and the Open Biomedical Ontologies (OBO) format, and summarizes features of each KR language. Guidelines on best practices for building bio-ontologies are suggested in BRIEFINGS IN BIOINFORMATICS. VOL 12. NO 1. 82^85 doi:10.1093/bib/bbq070 Advance Access published on 29 November 2010