Knihobot

Jinyan Li

    Data mining for biomedical applications
    Advanced Data Mining and Applications
    • Advanced Data Mining and Applications

      12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings

      • 817 stránek
      • 29 hodin čtení

      This book constitutes the proceedings of the 12th International Conference on Advanced Data Mining and Applications, ADMA 2016, held in Gold Coast, Australia, in December 2016. The 70 papers presented in this volume were carefully reviewed and selected from 105 submissions. The selected papers covered a wide variety of important topics in the area of data mining, including parallel and distributed data mining algorithms, mining on data streams, graph mining, spatial data mining, multimedia data mining, Web mining, the Internet of Things, health informatics, and biomedical data mining.

      Advanced Data Mining and Applications
    • Data mining for biomedical applications

      • 155 stránek
      • 6 hodin čtení

      InhaltsverzeichnisKeynote 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.

      Data mining for biomedical applications