Data Mining for Biomedical Applications

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.