Mehr lesen
Informationen zum Autor Yan-Qing Zhang, PhD, is an Associate Professor of Computer Science at the Georgia State University, Atlanta. His research interests include hybrid intelligent systems, neural networks, fuzzy logic, evolutionary computation, Yin-Yang computation, granular computing, kernel machines, bioinformatics, medical informatics, computational Web Intelligence, data mining, and knowledge discovery. He has coauthored two books, and edited one book and two IEEE proceedings. He is program co-chair of the IEEE 7th International Conference on Bioinformatics & Bioengineering (IEEE BIBE 2007) and 2006 IEEE International Conference on Granular Computing (IEEE-GrC2006). Jagath C. Rajapakse, PhD, is Professor of Computer Engineering and Director of the BioInformatics Research Centre, Nanyang Technological University. He is also Visiting Professor in the Department of Biological Engineering, Massachusetts Institute of Technology. He completed his MS and PhD degrees in electrical and computer engineering at University at Buffalo, State University of New York. Professor Rajapakse has published over 210 peer-reviewed research articles in the areas of neuroinformatics and bioinformatics. He serves as Associate Editor for IEEE Transactions on Medical Imaging and IEEE/ACM Transactions on Computational Biology and Bioinformatics. Klappentext An introduction to machine learning methods and their applications to problems in bioinformaticsMachine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization.From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more.Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels. Zusammenfassung An introduction to machine learning methods and their applications to problems in bioinformaticsMachine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization.From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinform...
