Biological Data Mining / Edition 1

Hardcover (Print)
Buy New
Buy New from BN.com
$85.64
Used and New from Other Sellers
Used and New from Other Sellers
from $39.07
Usually ships in 1-2 business days
(Save 66%)
Other sellers (Hardcover)
  • All (7) from $39.07   
  • New (3) from $103.06   
  • Used (4) from $39.07   

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.

Read More Show Less

Editorial Reviews

From the Publisher
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, the editors manage to maintain homogeneity throughout the book. I recommend this book as a valuable resource on biological data mining. The chapters offer a wealth of useful information …
Computing Reviews, January 2011

… Chen and Lonardi present in this book a showcase of successful recent projects in the research area where biology, computer science, and statistics intersect. The editors have done a good job of pulling together the work of over 80 authors into a well-typeset product with high-resolution graphics and even several diagrams of proteins. … The authors leave no stone unturned in terms of topics and techniques. … There is a veritable alphabet soup of special software employed … there is something for everyone with an interest in bioinformatics in this book. Make sure your library has a copy, or that you buy one for yourselves.
International Statistical Review (2010), 78, 3

Read More Show Less

Product Details

Meet the Author

Jake Y. Chen is an assistant professor of informatics at Indiana University, an assistant professor of computer science at Purdue University, and director of the Indiana Center for Systems Biology and Personalized Medicine.

Stefano Lonardi is an associate professor of computer science and engineering at the University of California, Riverside.

Read More Show Less

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

Read More Show Less

Customer Reviews

Be the first to write a review
( 0 )
Rating Distribution

5 Star

(0)

4 Star

(0)

3 Star

(0)

2 Star

(0)

1 Star

(0)

Your Rating:

Your Name: Create a Pen Name or

Barnes & Noble.com Review Rules

Our reader reviews allow you to share your comments on titles you liked, or didn't, with others. By submitting an online review, you are representing to Barnes & Noble.com that all information contained in your review is original and accurate in all respects, and that the submission of such content by you and the posting of such content by Barnes & Noble.com does not and will not violate the rights of any third party. Please follow the rules below to help ensure that your review can be posted.

Reviews by Our Customers Under the Age of 13

We highly value and respect everyone's opinion concerning the titles we offer. However, we cannot allow persons under the age of 13 to have accounts at BN.com or to post customer reviews. Please see our Terms of Use for more details.

What to exclude from your review:

Please do not write about reviews, commentary, or information posted on the product page. If you see any errors in the information on the product page, please send us an email.

Reviews should not contain any of the following:

  • - HTML tags, profanity, obscenities, vulgarities, or comments that defame anyone
  • - Time-sensitive information such as tour dates, signings, lectures, etc.
  • - Single-word reviews. Other people will read your review to discover why you liked or didn't like the title. Be descriptive.
  • - Comments focusing on the author or that may ruin the ending for others
  • - Phone numbers, addresses, URLs
  • - Pricing and availability information or alternative ordering information
  • - Advertisements or commercial solicitation

Reminder:

  • - By submitting a review, you grant to Barnes & Noble.com and its sublicensees the royalty-free, perpetual, irrevocable right and license to use the review in accordance with the Barnes & Noble.com Terms of Use.
  • - Barnes & Noble.com reserves the right not to post any review -- particularly those that do not follow the terms and conditions of these Rules. Barnes & Noble.com also reserves the right to remove any review at any time without notice.
  • - See Terms of Use for other conditions and disclaimers.
Search for Products You'd Like to Recommend

Recommend other products that relate to your review. Just search for them below and share!

Create a Pen Name

Your Pen Name is your unique identity on BN.com. It will appear on the reviews you write and other website activities. Your Pen Name cannot be edited, changed or deleted once submitted.

 
Your Pen Name can be any combination of alphanumeric characters (plus - and _), and must be at least two characters long.

Continue Anonymously

    If you find inappropriate content, please report it to Barnes & Noble
    Why is this product inappropriate?
    Comments (optional)