Applications of Machine Learning Techniques to Bioinformatics

The completion of the sequencing of the human genome was heralded the dawn of a new era in biology and medicine. Besides, advances in high-throughput experimental technologies, e.g. ChIP-chip, gene expression, and SNP microarrays, enable us to observe various aspects of biological processes. Such a global and comprehensive genomic view also changes the landscape of biological and biomedical research. This book is devoted to developing effective and efficient machine learning techniques for analyzing the huge amount of genetic data. Several important computational problems, from sequence annotation, microarray gene expression analysis, to optical mapping, are analyzed with novel machine learning methods. More importantly, this book places great emphasis on the interplay of biological observation and mathematical modeling, which, we believe, is the key to successfully apply machine learning techniques to bioinformatics. The analysis should help shed some light on this new and exciting area, and should be especially useful to professionals in Bioinformatics and Machine Learning fields.