Feature extraction via composite scoring and voting in breast cancer

Breast Cancer Research and Treatment

Volumn 135, Issue 1, 2012, Pages 307-318

  Koch, Martin ^a   Hanl, Markus ^a   Wiese, Michael ^a  

^a UNIVERSITY OF BONN   (Germany)

Author keywords

Classification; E2F4; Machine learning; Triple negative breast cancer; Tumor subtypes

Indexed keywords

ESTROGEN; TRANSCRIPTION FACTOR E2F4;

ADULT; AGED; ARTICLE; CANCER CLASSIFICATION; CANCER SURVIVAL; CLASSIFICATION ALGORITHM; CLUSTER ANALYSIS; DISTANT METASTASIS FREE SURVIVAL; E2F4 GENE; GENE EXPRESSION PROFILING; GENE SEQUENCE; HUMAN; MAJOR CLINICAL STUDY; MICROARRAY ANALYSIS; PREDICTION; PRIORITY JOURNAL; SCORING SYSTEM; TRIPLE NEGATIVE BREAST CANCER; TUMOR GENE;

ADULT; AGED; ANTINEOPLASTIC AGENTS, HORMONAL; BREAST NEOPLASMS; FEMALE; GENE EXPRESSION PROFILING; HUMANS; MIDDLE AGED; PROGNOSIS; RECEPTOR, EPIDERMAL GROWTH FACTOR; RECEPTORS, ESTROGEN; RECEPTORS, PROGESTERONE; TAMOXIFEN; TUMOR MARKERS, BIOLOGICAL;

EID: 84865112545     PISSN: 01676806     EISSN: 15737217     Source Type: Journal    
DOI: 10.1007/s10549-012-2177-3     Document Type: Article

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