Like a data-guzzling turbo engine, advanced data mining has been powering post-genome biological studies for two decades. Reflecting this growth, Biological Data Mining presents comprehensive data mining concepts, theories, and applications in current biological and medical research. Each chapter is written by a distinguished team of interdisciplinary data mining researchers who cover state-of-the-art biological topics. The first section of the book discusses challenges and opportunities in analyzing and mining biological sequences and structures to gain insight into molecular functions. The second section addresses emerging computational challenges in interpreting high-throughput Omics data. The book then describes the relationships between data mining and related areas of computing, including knowledge representation, information retrieval, and data integration for structured and unstructured biological data. The last part explores emerging data mining opportunities for biomedical applications. This volume examines the concepts, problems, progress, and trends in developing and applying new data mining techniques to the rapidly growing field of genome biology. By studying the concepts and case studies presented, readers will gain significant insight and develop practical solutions for similar biological data mining projects in the future.

Thisvolumecontainsthe paperspresentedatthe 17thInternationalSymposium on String Processing and Information Retrieval (SPIRE 2010), held October 11-13, 2010 in Los Cabos, Mexico. The annual SPIRE conference provides researchers within ?elds related to string processing and/or information retrieval a possibility to present their or- inal contributions and to meet and talk with other researchers with similar - terests. The call for papers invited submissions related to string processing (d- tionary algorithms; text searching; pattern matching; text and sequence c- pression; automata-based string processing), information retrieval (information retrieval models; indexing; ranking and ?ltering; querying and interface design), natural language processing (text analysis; text mining; machine learning; - formation extraction; language models; knowledge representation), searchapp- cations and usage (cross-lingual information access systems; multimedia inf- mation access; digital libraries; collaborative retrieval and Web-related appli- tions; semi-structured data retrieval; evaluation), and interaction of biology and computation (DNA sequencing and applications in molecular biology; evolution andphylogenetics;recognitionofgenesandregulatoryelements;sequencedriven protein structure prediction). The papers presented at the symposium were selected from 109 submissions written by authors from 30 di?erent countries. Each submission was reviewed by at least three reviewers, with a maximum of ?ve reviews for particularly challengingpapers. The ProgramCommittee accepted 39 papers(corresponding to ?35% acceptance rate): 26 long papers and 13 short papers. In addition to these presentations, SPIRE 2010 also featured invited talks by Gonzalo Navarro (Universidad de Chile) and Mark Najork (Microsoft Research, USA).

Like a data-guzzling turbo engine, advanced data mining has been powering post-genome biological studies for two decades. Reflecting this growth, Biological Data Mining presents comprehensive data mining concepts, theories, and applications in current biological and medical research. Each chapter is written by a distinguished team of interdisciplinary data mining researchers who cover state-of-the-art biological topics. The first section of the book discusses challenges and opportunities in analyzing and mining biological sequences and structures to gain insight into molecular functions. The second section addresses emerging computational challenges in interpreting high-throughput Omics data. The book then describes the relationships between data mining and related areas of computing, including knowledge representation, information retrieval, and data integration for structured and unstructured biological data. The last part explores emerging data mining opportunities for biomedical applications. This volume examines the concepts, problems, progress, and trends in developing and applying new data mining techniques to the rapidly growing field of genome biology. By studying the concepts and case studies presented, readers will gain significant insight and develop practical solutions for similar biological data mining projects in the future.