Data Mining for Biomedical Applications : PAKDD 2006 Workshop, BioDM 2006, Singapore, April 9, 2006, Proceedings - Jinyan Li

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Data Mining for Biomedical Applications

PAKDD 2006 Workshop, BioDM 2006, Singapore, April 9, 2006, Proceedings

By: Jinyan Li (Editor), Qiang Yang (Editor), Ah-Hwee Tan (Editor)

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Paperback | 23 March 2006

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168 Pages

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This book constitutes the refereed proceedings of the International Workshop on Data Mining for Biomedical Applications, BioDM 2006, held in Singapore in conjunction with the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006). The 14 revised full papers presented together with one keynote talk were carefully reviewed and selected from 35 submissions. The papers are organized in topical sections

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You Can Find This Book In

Non-Fiction Computing & I.T.Databases Data Mining Computer Science Artificial Intelligence Machine Learning Medicine Medicine in General Health Systems & Services Medical Administration & Management Business Applications

Mathematical & Statistical Software Science Biology, Life Sciences Life Sciences in General General Practice

Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions	p. 1
A database search algorithm for identification of peptides with multiple charges using tandem mass spectrometry	p. 2
Filtering bio-sequence based on sequence descriptor	p. 14
Automatic extraction of genomic glossary triggered by query	p. 24
Frequent subsequence-based protein localization	p. 35
gTRICLUSTER : a more general and effective 3D clustering algorithm for gene-sample-time microarray data	p. 48
Automatic orthologus-protein-clustering from multiple complete-genomes by the best reciprocal BLAST hits	p. 60
A novel clustering method for analysis of gene microarray expression data	p. 71
Heterogeneous clustering ensemble method for combining different cluster results	p. 82
Rule learning for disease-specific biomarker discovery from clinical proteomic mass spectra	p. 93
Machine learning techniques and chi-square feature selection for cancer classification using SAGE gene expression profiles	p. 106
Generation of comprehensible hypotheses from gene expression data	p. 116
Classification of brain glioma by using SVMs bagging with feature selection	p. 124
Missing value imputation framework for microarray significant gene selection for class prediction	p. 131
Informative MicroRNA expression patterns for cancer classification	p. 143
Table of Contents provided by Blackwell. All Rights Reserved.

Data Mining for Biomedical Applications

PAKDD 2006 Workshop, BioDM 2006, Singapore, April 9, 2006, Proceedings

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Paperback

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