Data mining for biomedical applications : PAKDD 2006 workshop, BioDM 2006, Singapore, April 9, 2006 : proceedings / / Jinyan Li, Qiang Yang, Ah-Hwee Tan (eds.)

• Record Nr.: UNINA9910767587903321
• Pubbl/distr/stampa: Berlin, : Springer, c2006
• ISBN: 3-540-33105-0
• Edizione: [1st ed. 2006.]
• Descrizione fisica: 1 online resource (VIII, 155 p.)
• Collana: LNCS sublibrary. SL 8, Bioinformatics; Lecture notes in computer science, , 0302-9743 ; ; 3916. Lecture notes in bioinformatics
• Altri autori (Persone): LiJinyan, Ph. D.; YangQiang <1961->; TanAh-Hwee
• Disciplina: 610.285
• Soggetti: Medical informatics; Data mining; Artificial intelligence - Medical applications
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: "... papers selected for presentation at the First Workshop on Data Mining for Biomedical Applications (BioDM 2006) ... in conjunction with the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2006)"--Pref.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: 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.