Development of Machine Learning Models to Predict Platinum Sensitivity of High-Grade Serous Ovarian Carcinoma.

Hwangbo, Suhyun; Kim, Se Ik; Kim, Ju-Hyun; Eoh, Kyung Jin; Lee, Chanhee; Kim, Young Tae; Suh, Dae-Shik; Park, Taesung; Song, Yong Sang

Hwangbo, Suhyun; Kim, Se Ik; Kim, Ju-Hyun; Eoh, Kyung Jin; Lee, Chanhee; Kim, Young Tae; Suh, Dae-Shik; Park, Taesung; Song, Yong Sang.

Afiliação

Hwangbo S; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.
Kim SI; Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul 03080, Korea.
Kim JH; Department of Obstetrics and Gynecology, Graduate School of Medicine, University of Ulsan, Seoul 05505, Korea.
Eoh KJ; Department of Obstetrics and Gynecology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin-si 17046, Korea.
Lee C; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.
Kim YT; Department of Obstetrics and Gynecology, Institute of Women's Life Medical Science, Yonsei University College of Medicine, Seoul 03722, Korea.
Suh DS; Department of Obstetrics and Gynecology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea.
Park T; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.
Song YS; Department of Statistics, Seoul National University, Seoul 08826, Korea.

Cancers (Basel) ; 13(8)2021 Apr 14.

Article em En | MEDLINE | ID: mdl-33919797

Palavras-chave

high-grade serous carcinoma; machine learning; model; ovarian cancer; platinum resistance; prognosis

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Cancers (Basel) Ano de publicação: 2021 Tipo de documento: Article

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google