Data Mining for Biomedical Applications
PAKDD 2006 Workshop, BioDM 2006, Singapore, April 9, 2006, Proceedings
Published on 23. March 2006
Book
Paperback/Softback
VIII, 155 pages
978-3-540-33104-9 (ISBN)
Description
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
More details
Series
Edition
2006 ed.
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Research
Illustrations
VIII, 155 p.
Dimensions
Height: 23.5 cm
Width: 15.5 cm
Weight
540 gr
ISBN-13
978-3-540-33104-9 (9783540331049)
DOI
10.1007/11691730
Schweitzer Classification
Content
Keynote Talk.- Exploiting Indirect Neighbours and Topological Weight to Predict Protein Function from Protein-Protein Interactions.- Database and Search.- A Database Search Algorithm for Identification of Peptides with Multiple Charges Using Tandem Mass Spectrometry.- Filtering Bio-sequence Based on Sequence Descriptor.- Automatic Extraction of Genomic Glossary Triggered by Query.- Frequent Subsequence-Based Protein Localization.- Bio Data Clustering.- gTRICLUSTER: A More General and Effective 3D Clustering Algorithm for Gene-Sample-Time Microarray Data.- Automatic Orthologous-Protein-Clustering from Multiple Complete-Genomes by the Best Reciprocal BLAST Hits.- A Novel Clustering Method for Analysis of Gene Microarray Expression Data.- Heterogeneous Clustering Ensemble Method for Combining Different Cluster Results.- In-silico Diagnosis.- Rule Learning for Disease-Specific Biomarker Discovery from Clinical Proteomic Mass Spectra.- Machine Learning Techniques and Chi-Square Feature Selection for Cancer Classification Using SAGE Gene Expression Profiles.- Generation of Comprehensible Hypotheses from Gene Expression Data.- Classification of Brain Glioma by Using SVMs Bagging with Feature Selection.- Missing Value Imputation Framework for Microarray Significant Gene Selection and Class Prediction.- Informative MicroRNA Expression Patterns for Cancer Classification.