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

Li, Jinyan

ISBN 10: 3540331042 / ISBN 13: 9783540331049

Editore: Springer 2006-03, 2006

Riguardo questo articolo

Codice articolo 6666-IUK-9783540331049

Dati bibliografici

• Titolo: Data Mining for Biomedical Applications : PAKDD 2006 Workshop, BioDM 2006, Singapore, April 9, 2006, Proceedings
• Editore: Springer 2006-03
• Data di pubblicazione: 2006
• Lingua: Inglese
• ISBN 10: 3540331042
• ISBN 13: 9783540331049
• Rilegatura: PF
• Condizione: New

Descrizione articolo

Riassunto

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

Contenuti

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.