Bayesian evolutionary hypergraph learning for predicting cancer clinical outcomes.

Kim, Soo-Jin; Ha, Jung-Woo; Zhang, Byoung-Tak

Kim, Soo-Jin; Ha, Jung-Woo; Zhang, Byoung-Tak.

Afiliação

Kim SJ; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-742, Republic of Korea; Center for Biointelligence Technology (CBIT), Seoul National University, Seoul 151-742, Republic of Korea. Electronic address: sjkim@bi.snu.ac.kr.
Ha JW; Center for Biointelligence Technology (CBIT), Seoul National University, Seoul 151-742, Republic of Korea; School of Computer Science and Engineering, Seoul National University, Seoul 151-742, Republic of Korea. Electronic address: jwha@bi.snu.ac.kr.
Zhang BT; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-742, Republic of Korea; Center for Biointelligence Technology (CBIT), Seoul National University, Seoul 151-742, Republic of Korea; School of Computer Science and Engineering, Seoul National University, Seoul 151-742, Republic of Korea. Electronic address: btzhang@bi.snu.ac.kr.

J Biomed Inform ; 49: 101-11, 2014 Jun.

Article em En | MEDLINE | ID: mdl-24524888

Assuntos

Teorema de Bayes; Aprendizagem; Neoplasias/terapia; Humanos; Neoplasias/patologia; Resultado do Tratamento

Palavras-chave

Bayesian evolutionary learning; Cancer genomic data; Clinical outcome prediction; Hypergraph classifier

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Teorema de Bayes / Aprendizagem / Neoplasias Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2014 Tipo de documento: Article

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google