Description
An introduction to machine learning methods and their applicationsto problems in bioinformatics

Machine learning techniques are increasingly being used toaddress problems in computational biology and bioinformatics. Novelcomputational techniques to analyze high throughput data in theform of sequences, gene and protein expressions, pathways, andimages are becoming vital for understanding diseases and futuredrug discovery. Machine learning techniques such as Markov models,support vector machines, neural networks, and graphical models havebeen successful in analyzing life science data because of theircapabilities in handling randomness and uncertainty of data noiseand in generalization.

From an internationally recognized panel of prominentresearchers in the field, Machine Learning in Bioinformaticscompiles recent approaches in machine learning methods and theirapplications in addressing contemporary problems in bioinformatics.Coverage includes: feature selection for genomic and proteomic datamining; comparing variable selection methods in gene selection andclassification of microarray data; fuzzy gene mining;sequence-based prediction of residue-level properties in proteins;probabilistic methods for long-range features in biosequences; andmuch more.

Machine Learning in Bioinformatics is an indispensable resourcefor computer scientists, engineers, biologists, mathematicians,researchers, clinicians, physicians, and medical informaticists. Itis also a valuable reference text for computer science,engineering, and biology courses at the upper undergraduate andgraduate levels.