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Biological data mining

Author: Jake Chen; Stefano Lonardi
Publisher: Boca Raton, FL : Chapman & Hall/CRC, ©2010.
Series: Chapman & Hall/CRC data mining and knowledge discovery series.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
"This volume examines the concepts, problems, progress, and trends in developing and applying new data mining techniques to the rapidly growing field of genome biology. By studying the concepts and case studies presented, readers will gain significant insight and develop practical solutions for similar biological data mining projects in the future."--Jacket.
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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Jake Chen; Stefano Lonardi
ISBN: 9781420086843 1420086847
OCLC Number: 226357310
Description: xx, 713 pages : illustrations ; 25 cm.
Contents: Consensus structure prediction for RNA alignments / Junilda Spirollari and Jason T.L. Wang --
Invariant geometric properties of secondary structure elements in proteins / Matteo Comin, Concettina Guerra, and Giuseppe Zanotti --
Discovering 3D motifs in RNA / Alberto Apostolico [and others] --
Protein structure classification using machine learning methods / Yazhene Krishnaraj and Chandan Reddy --
Protein surface representation and comparison: new approaches in structural proteomics / Lee Sael and Daisuke Kihara --
Advanced graph mining methods for protein analysis / Yi-Ping Phoebe Chen, Jia Rong, and Gang Li --
Predicting local structure and function of proteins / Huzefa Rangwala and George Karypis --
Computational approaches for genome assembly validation / Jeong-Hyeon Choi [and others] --
Mining patterns of epistasis in human genetics / Jason H. Moore --
Discovery of regulatory mechanisms from gene expression variation by eQTL analysis / Yang Huang, Jie Zheng, and Teresa M. Przytycka --
Statistical approaches to gene expression microarray data preprocessing / Megan Kong [and others] --
Application of feature selection and classification to computational molecular biology / Paola Bertolazzi, Giovanni Felici, and Giuseppe Lancia --
Statistical indices for computational and data driven class discovery in microarray data / Raffaele Giancarlo, Davide Scaturro, and Filippo Utro --
Computational approaches to peptide retention time prediction for proteomics / Xiang Zhang [and others] --
Inferring protein functional linkage based on sequence information and beyond / Li Liao --
Computational methods for unraveling transcriptional regulatory networks in prokaryotes / Dongsheng Che and Guojun Li --
Computational methods for analyzing and modeling biological networks / Nataša Pržulj and Tijana Milenković --
Statistical analysis of biomolecular networks / Jing-Dong J. Han and Chris J. Needham --
Beyond information retrieval: literature mining for biomedical knowledge discovery / Javed Mostafa, Kazuhiro Seki, and Weimao Ke --
Mining biological interactions from biomedical texts for efficient query answering / Muhammad Abulaish, Lipika Dey, and Jahiruddin --
Ontology-based knowledge representation of experiment metadata in biological data mining / Richard H. Scheuermann [and others] --
Redescription mining and applications in bioinformatics / Naren Ramakrishnan and Mohammed J. Zaki --
Data mining tools and techniques for identification of biomarkers for cancer / Mick Correll [and others] --
Cancer biomarker prioritization: assessing the in vivo impact of in vitro models by in silico mining of microarray database, literature, and gene annotation / Chia-Ju Lee [and others] --
Biomarker discovery by mining glycomic and lipidomic data / Haixu Tang, Mehmet Dalkilic, and Yehia Mechref --
Data mining chemical structures and biolobical data / Glenn J. Myatt and Paul E. Blower.
Series Title: Chapman & Hall/CRC data mining and knowledge discovery series.
Responsibility: edited by Jake Y. Chen, Stefano Lonardi.

Abstract:

Presents comprehensive data mining concepts, theories and applications in biological and medical research. This book discusses challenge and opportunities in analyzing and mining biological sequences  Read more...

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The book will be useful to those interested in applying data mining to biology. Specialists in interdisciplinary areas will also find the book helpful. Despite the diversity of the topics presented, Read more...

 
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