Biological data mining:
"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...
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Boca Raton
Chapman & Hall
2010
|
| Schriftenreihe: | Chapman & Hall/ CRC data mining and knowledge discovery series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Zusammenfassung: | "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."--BOOK JACKET. |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XX, 713 S. Ill., graph. Darst. |
| ISBN: | 9781420086843 1420086847 |
Internformat
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Datensatz im Suchindex
| _version_ | 1804140717847609344 |
|---|---|
| adam_text | Contents
Preface
ix
Editors xiii
Contributors
xv
Part I Sequence, Structure, and Function
1
1
Consensus Structure Prediction for
RNA
Alignments
3
Junilda Spirollari and Jason T. L. Wang
2
Invariant Geometric Properties of Secondary Structure
Elements in Proteins
27
Matteo Comin, Concettina
Guerra,
and Giuseppe Zanotti
3
Discovering
3D
Motifs in
RNA
49
Alberto
Apostolico,
Giovanni Ciriello, Concettina
Guerra,
and Christine E. Heitsch
4
Protein Structure Classification Using Machine
Learning Methods
69
Yazhene Krishnaraj and Chandan Reddy
5
Protein Surface Representation and Comparison:
New Approaches in Structural Proteomics
89
Lee Sael and Daisuke Kihara
6
Advanced Graph Mining Methods for Protein Analysis 111
Yi-Ping Phoebe Chen, Jia Rong, and Gang Li
7
Predicting Local Structure and Function of Proteins
137
Huzefa Rangwala and George Karypis
vi
Contents
Part II Genomics, Transcriptomics,
and Proteomics
161
8
Computational Approaches for Genome Assembly
Validation
163
Jeong-Hyeon Choi, Haixu Tang, Sun Kim, and Mihai Pop
9
Mining Patterns of Epistasis in Human Genetics
187
Jason H. Moore
10
Discovery of Regulatory Mechanisms from Gene
Expression Variation by eQTL Analysis
205
Yang Huang, Jie Zheng, and Teresa M. Przytycka
11
Statistical Approaches to Gene Expression Microarray
Data Preprocessing
229
Megan Kong, Elizabeth McClellan, Richard H.
Scheuermann,
and Monnie McGee
12
Application of Feature Selection and Classification
to Computational Molecular Biology
257
Paola Bertolazzi,
Giovanni
Felici,
and Giuseppe Lancia
13
Statistical Indices for Computational and Data Driven
Class Discovery in Microarray Data
295
Raffaele Giuncarlo, Davide Scaturro,
and
Filippo
Utro
14
Computational Approaches to
Peptide
Retention Time
Prediction for Proteomics
337
Xiang Zhang, Cheolhwan Oh, Catherine P. Riley,
Hyeyoung Cho, and Charles Buck
Part III Functional and Molecular Interaction
Networks
351
15
Inferring Protein Functional Linkage Based on Sequence
Information and Beyond
353
Li Liao
16
Computational Methods for Unraveling Iranscriptional
Regulatory Networks in Prokaryotes
377
Dongsheng Che and Guojun Li
17
Computational Methods for Analyzing and Modeling
Biological Networks
397
Nataša Pržulj
and
Tíjana
Milenković
Contents
vii
18
Statistical Analysis of Biomolecular Networks
429
Jing-Dong J. Han and Chris J. Needham
Part IV Literature, Ontology, and Knowledge
Integration
447
19
Beyond Information Retrieval: Literature Mining
for
Biomedical
Knowledge Discovery
449
Javed Mostafa, Kazuhiro Seki, and Weimao Ke
20
Mining Biological Interactions from
Biomedical
Texts
for Efficient Query Answering
485
Muhammad Abulaish, Lipika
Dey,
and Jahiruddin
21
Ontology-Based Knowledge Representation of Experiment
Metadata in Biological Data Mining
529
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
22
Redescription
Mining and Applications in Bioinformatics
561
Naren
Ramakrishnan and Mohammed J. Zaki
Part V Genome Medicine Applications
587
23
Data Mining Tools and Techniques for Identification of
Biomarkers
for Cancer
589
Mick
Correli,
Simon Beaulah, Robin Munro, Jonathan Sheldon,
Yike Guo, and
Наг Ни
24
Cancer
Biomarker Prioritization:
Assessing the in vivo
Impact of in vitro Models by in silico Mining
of Microarray Database, Literature, and Gene Annotation
615
Chia-Ju Lee, Zan Huang, Hongmei Jiang, John Crispino,
and Simon Lin
25
Biomarker
Discovery by Mining Glycomic and
Lipidomic Data
627
Haixu
Tang, Mehmet Dalkilic,
and Yehia Mechref
26
Data Mining Chemical Structures and Biological Data
649
Glenn J. Myatt and Paul E. Blower
Index
689
Chapman
& Hall/CRC
Data Mining and Knowledge Discovery Series
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 challenge 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.
Features
•
Provides comprehensive coverage of biological sequences, structures. Omics.
ontology, literature mining, and
biomedical
applications
•
Integrates biological concepts with computational data mining techniques
•
Includes case studies of biological applications
•
Presents both basic concepts and advanced techniques
•
Incorporates contributions from a team of experts who are active in biological
data mining research
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
practicai soîutions
for similar biofogical data mining
projects in the future.
|
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| discipline | Biologie Informatik |
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| title | Biological data mining |
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| title_fullStr | Biological data mining ed. by Jake Y. Chen ... |
| title_full_unstemmed | Biological data mining ed. by Jake Y. Chen ... |
| title_short | Biological data mining |
| title_sort | biological data mining |
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