Conditional independence relations among biological markers may improve clinical decision as in the case of triple negative breast cancers

BMC Bioinformatics

Volumn 10, Issue SUPPL. 12, 2009, Pages

  Stefanini, Federico M ^a   Coradini, Danila ^b   Biganzoli, Elia ^b  

^a UNIVERSITY OF FLORENCE   (Italy)

^b UNIVERSITY OF MILAN   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORKS; BIOMARKERS; CHEMOTHERAPY; PROTEINS;

CONDITIONAL DISTRIBUTION; CONDITIONAL INDEPENDENCES; ENVIRONMENTAL DETERMINANTS; MOLECULAR INFORMATION; MOLECULAR PHENOTYPES; MULTIVARIATE DISTRIBUTIONS; PROGESTERONE RECEPTOR; TRIPLE-NEGATIVE BREAST CANCERS;

DISEASES;

ALKYLATING AGENT; ANTHRACYCLINE DERIVATIVE; BIOLOGICAL MARKER; EPIDERMAL GROWTH FACTOR RECEPTOR 2; ESTROGEN RECEPTOR; KI 67 ANTIGEN; PROGESTERONE RECEPTOR; PROTEIN P53;

ADJUVANT THERAPY; ARTICLE; BAYES THEOREM; BREAST CANCER; CANCER CHEMOTHERAPY; CANCER SURGERY; CLINICAL DECISION MAKING; HUMAN; HUMAN TISSUE; LEARNING; PATIENT SELECTION; PHENOTYPE; PROBABILITY; PROTEIN EXPRESSION; PROTEIN LOCALIZATION; SCORING SYSTEM; TRIPLE NEGATIVE BREAST CANCER;

EID: 70449516824     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-10-S12-S13     Document Type: Article

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