A clustering based hybrid system for biomarker selection and sample classification of mass spectrometry data

Neurocomputing

Volumn 73, Issue 13-15, 2010, Pages 2317-2331

  Yang, Pengyi ^a,b   Zhang, Zili ^c,d   Zhou, Bing B ^a   Zomaya, Albert Y ^a  

^a UNIVERSITY OF SYDNEY   (Australia)

^b CSIRO   (Australia)

^c SOUTHWEST UNIVERSITY   (China)

^d DEAKIN UNIVERSITY   (Australia)

Author keywords

Classification; Clustering; Feature selection; Hybrid algorithm; Mass spectrometry

Indexed keywords

BIO-MARKER DISCOVERY; CLUSTERING; CLUSTERING FEATURE; DATA MINING TECHNIQUES; EARLY DETECTION; FEATURE SELECTION; FEATURE SELECTION ALGORITHM; HYBRID ALGORITHMS; ITERATIVE PROCEDURES; K-MEANS CLUSTERING; LOWER CORRELATION; MASS SPECTROMETRY DATA; PROTEOMES; PROTEOMICS; SAMPLE CLASSIFICATION; STOCHASTIC NATURE; WRAPPER-BASED FEATURE SELECTION;

CLUSTER ANALYSIS; DATA MINING; ELECTRIC GROUNDING; FEATURE EXTRACTION; FILTRATION; HYBRID COMPUTERS; HYBRID SYSTEMS; MASS SPECTROMETRY; MOLECULAR BIOLOGY; WATER SUPPLY SYSTEMS;

CLUSTERING ALGORITHMS;

ACCURACY; ARTICLE; CLASSIFIER; CONTROLLED STUDY; GENE CLUSTER; GENE IDENTIFICATION; GENETIC ALGORITHM; GENETIC DATABASE; GENETIC MARKER; GENETIC SELECTION; GENOMIC INSTABILITY; MASS SPECTROMETRY; MATHEMATICAL MODEL; PRIORITY JOURNAL; PROTEOMICS; SYSTEM ANALYSIS; TWO HYBRID SYSTEM;

EID: 77955325026     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2010.02.022     Document Type: Article

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