Zhang / Rajapakse Machine Learning in Bioinformatics

1. Auflage 2009
ISBN: 978-0-470-39741-1
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 480 Seiten, E-Book

Reihe: Wiley Series in Bioinformatics

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.

Autoren/Hrsg.

Zhang, Yanqing

Rajapakse, Jagath C.

Inhaltsverzeichnis

Foreword.
Preface.
Contributors.
1 Feature Selection for Genomic and Proteomic Data Mining(Sun-Yuan Kung and Man-Wai Mak).
2 Comparing and Visualizing Gene Selection and ClassificationMethods for Microarray Data (Rajiv S. Menjoge and Roy E.Welsch).
3 Adaptive Kernel Classifiers Via Matrix Decomposition Updatingfor Biological Data Analysis (Hyunsoo Kim and HaesunPark).
4 Bootstrapping Consistency Method for Optimal Gene Selectionfrom Microarray Gene Expression Data for Classification Problems(Shaoning Pang, Ilkka Havukkala, Yingjie Hu, and NikolaKasabov).
5 Fuzzy Gene Mining: A Fuzzy-Based Framework for CancerMicroarray Data Analysis (Zhenyu Wang and VasilePalade).
6 Feature Selection for Ensemble Learning and Its Application(Guo-Zheng Li and Jack Y. Yang).
7 Sequence-Based Prediction of Residue-Level Properties inProteins (Shandar Ahmad, Yemlembam Hemjit Singh, Marcos J.Araúzo-Bravo, and Akinori Sarai).
8 Consensus Approaches to Protein Structure Prediction(Dongbo Bu, ShuaiCheng Li, Xin Gao, Libo Yu, Jinbo Xu, and MingLi).
9 Kernel Methods in Protein Structure Prediction(Jayavardhana Gubbi, Alistair Shilton, and MarimuthuPalaniswami).
10 Evolutionary Granular Kernel Trees for Protein SubcellularLocation Prediction (Bo Jin and Yan-Qing Zhang).
11 Probabilistic Models for Long-Range Features in Biosequences(Li Liao).
12 Neighborhood Profile Search for Motif Refinement (ChandanK. Reddy, Yao-Chung Weng, and Hsiao-Dong Chiang).
13 Markov/Neural Model for Eukaryotic Promoter Recognition(Jagath C. Rajapakse and Sy Loi Ho).
14 Eukaryotic Promoter Detection Based on Word and SequenceFeature Selection and Combination (Xudong Xie, Shuanhu Wu, andHong Yan).
15 Feature Characterization and Testing of BidirectionalPromoters in the Human Genome--Significance and Applicationsin Human Genome Research (Mary Q. Yang, David C. King, and LauraL. Elnitski).
16 Supervised Learning Methods for MicroRNA Studies(Byoung-Tak Zhang and Jin-Wu Nam).
17 Machine Learning for Computational Haplotype Analysis(Phil H. Lee and Hagit Shatkay).
18 Machine Learning Applications in SNP-DiseaseAssociation Study (Pritam Chanda, Aidong Zhang, and MuraliRamanathan).
19 Nanopore Cheminformatics-Based Studies of IndividualMolecular Interactions (Stephen Winters-Hilt).
20 An Information Fusion Framework for Biomedical Informatics(Srivatsava R. Ganta, Anand Narasimhamurthy, Jyotsna Kasturi,and Raj Acharya).
Index.

Über Autor(innen)

Yan-Qing Zhang, PhD, is an Associate Professor of ComputerScience at the Georgia State University, Atlanta. His researchinterests include hybrid intelligent systems, neural networks,fuzzy logic, evolutionary computation, Yin-Yang computation,granular computing, kernel machines, bioinformatics, medicalinformatics, computational Web Intelligence, data mining, andknowledge discovery. He has coauthored two books, and edited onebook and two IEEE proceedings. He is program co-chair of the IEEE7th International Conference on Bioinformatics & Bioengineering(IEEE BIBE 2007) and 2006 IEEE International Conference on GranularComputing (IEEE-GrC2006).
Jagath C. Rajapakse, PhD, is Professor of Computer Engineeringand Director of the BioInformatics Research Centre, NanyangTechnological University. He is also Visiting Professor in theDepartment of Biological Engineering, Massachusetts Institute ofTechnology. He completed his MS and PhD degrees in electrical andcomputer engineering at University at Buffalo, State University ofNew York. Professor Rajapakse has published over 210 peer-reviewedresearch articles in the areas of neuroinformatics andbioinformatics. He serves as Associate Editor for IEEE Transactionson Medical Imaging and IEEE/ACM Transactions on ComputationalBiology and Bioinformatics.