Biological Data Mining / Edition 1 by Jake Y. Chen | 9781420086843 | Hardcover | Barnes & Noble
Biological Data Mining / Edition 1

Biological Data Mining / Edition 1

by Jake Y. Chen
     
 

ISBN-10: 1420086847

ISBN-13: 9781420086843

Pub. Date: 09/08/2009

Publisher: Taylor & Francis

Like a data-guzzling turbo engine, advanced data mining has been powering post-genome biological studies for two decades. Reflecting this growth, Biological Data Mining presents comprehensive data mining concepts, theories, and applications in current biological and medical research. Each chapter is written by a distinguished team of interdisciplinary data mining

Overview

Like a data-guzzling turbo engine, advanced data mining has been powering post-genome biological studies for two decades. Reflecting this growth, Biological Data Mining presents comprehensive data mining concepts, theories, and applications in current biological and medical research. Each chapter is written by a distinguished team of interdisciplinary data mining researchers who cover state-of-the-art biological topics. The first section of the book discusses challenges and opportunities in analyzing and mining biological sequences and structures to gain insight into molecular functions. The second section addresses emerging computational challenges in interpreting high-throughput Omics data. The book then describes the relationships between data mining and related areas of computing, including knowledge representation, information retrieval, and data integration for structured and unstructured biological data. The last part explores emerging data mining opportunities for biomedical applications. 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.

Product Details

ISBN-13:
9781420086843
Publisher:
Taylor & Francis
Publication date:
09/08/2009
Series:
Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, #11
Edition description:
New Edition
Pages:
733
Product dimensions:
6.20(w) x 9.40(h) x 1.70(d)

Table of Contents

SEQUENCE, STRUCTURE, AND FUNCTION

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, Giovanni Ciriello, Christine E. Heitsch, and Concettina Guerra

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

GENOMICS, TRANSCRIPTOMICS, AND PROTEOMICS

Computational Approaches for Genome Assembly Validation

Jeong-Hyeon Choi, Haixu Tang, Sun Kim, and Mihai Pop

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, Elizabeth McClellan, Richard H. Scheuermann, and Monnie McGee

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, Cheolhwan Oh, Catherine P. Riley, Hyeyoung Cho, and Charles Buck

FUNCTIONAL AND MOLECULAR INTERACTION NETWORKS

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

LITERATURE, ONTOLOGY, AND KNOWLEDGE INTEGRATION

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, Megan Kong, Carl Dahlke, Jennifer Cai, Jamie Lee, Yu Qian, Burke Squires, Patrick Dunn, Jeff Wiser, Herb Hagler, Barry Smith, and David Karp

Redescription Mining and Applications in Bioinformatics

Naren Ramakrishnan and Mohammed J. Zaki

GENOME MEDICINE APPLICATIONS

Data Mining Tools and Techniques for Identification of Biomarkers for Cancer

Mick Correll, Simon Beaulah, Robin Munro, Jonathan Sheldon, Yike Guo, and Hai Hu

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, Zan Huang, Hongmei Jiang, John Crispino, and Simon Lin

Biomarker Discovery by Mining Glycomic and Lipidomic Data

Haixu Tang, Mehmet Dalkilic, and Yehia Mechref

Data Mining Chemical Structures and Biological Data

Glenn J. Myatt and Paul E. Blower

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