Data Mining for Biomedical Applications

Inhaltsverzeichnis

• 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.