The field of bioinformatics has two main objectives: the creation and maintenance of biological databases, and the discovery of knowledge from life sciences data in order to unravel the mysteries of biological function, leading to new drugs and therapies for human disease. Life sciences data come in the form of biological sequences, structures, pathways, or literature. One major aspect of discovering biological knowledge is to search, predict, or model specific patterns of a given dataset, which have some relevance to an important biological phenomenon or another dataset. To date, many pattern recognition algorithms have been applied or catered to address a wide range of bioinformatics problems. The 2006 Workshop of Bioinformatics in Pattern Recognition (PRIB 2006) marks the beginning of a series of workshops that is aimed at gathering researchers applying pattern recognition algorithms in an attempt to resolve problems in computational biology and bioinformatics. This volume presentsthe proceedings of Workshop PRIB 2006 held in Hong Kong, China, on August 20, 2006. It includes 19 technical contributions that were selected by the Program Committee from 43 submissions. We give a brief introduction to pattern recognition in bioinformatics in the first paper. The rest of the volume consists of three parts. Part 1: signal and motif detection, and gene selection. Part 2: models of DNA, RNA, and protein structures. Part 3: biological databases and imaging.
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This book constitutes the refereed proceedings of the International Workshop on Pattern Recognition in Bioinformatics, PRIB 2006, held in Hong Kong, China in August 2006 within the scope of ICPR 2006, the 18th International Conference on Pattern Recognition. The 19 revised full papers presented together with a introduction on Pattern Recognition in Bioinformatics were carefully reviewed and selected from 43 submissions. The papers cover all topics of the creation and maintenance of biological databases, and the discovery of knowledge from life sciences data. The papers are organized in 3 parts - the first on detection of signals, motifs, and gene structure of genomic sequences and gene selection from microarray data. The second part focuses on the prediction of different models of DNA, RNA, and amino acids to predict protein secondary structure, protein subcellular localization, RNA structure, phylogeny, and nucleosome formation and the third part deals with biological databases and images.
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