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Machine learning with in silico analysis markedly improves survival prediction modeling in colon cancer patients.
Lee, Choong-Jae; Baek, Bin; Cho, Sang Hee; Jang, Tae-Young; Jeon, So-El; Lee, Sunjae; Lee, Hyunju; Nam, Jeong-Seok.
Afiliación
  • Lee CJ; School of Life Sciences, Gwangju Institute of Science and Technology, Gwangju, Korea.
  • Baek B; School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Korea.
  • Cho SH; Department of Hemato-Oncology, Chonnam National University Medical School, Gwangju, Korea.
  • Jang TY; School of Life Sciences, Gwangju Institute of Science and Technology, Gwangju, Korea.
  • Jeon SE; School of Life Sciences, Gwangju Institute of Science and Technology, Gwangju, Korea.
  • Lee S; School of Life Sciences, Gwangju Institute of Science and Technology, Gwangju, Korea.
  • Lee H; School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, Korea.
  • Nam JS; School of Life Sciences, Gwangju Institute of Science and Technology, Gwangju, Korea.
Cancer Med ; 12(6): 7603-7615, 2023 03.
Article en En | MEDLINE | ID: mdl-36345155
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias del Colon / Variaciones en el Número de Copia de ADN Tipo de estudio: Guideline / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Cancer Med Año: 2023 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias del Colon / Variaciones en el Número de Copia de ADN Tipo de estudio: Guideline / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Cancer Med Año: 2023 Tipo del documento: Article Pais de publicación: Estados Unidos