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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.
Affiliation
  • 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 in En | MEDLINE | ID: mdl-36345155
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Colonic Neoplasms / DNA Copy Number Variations Type of study: Guideline / Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Cancer Med Year: 2023 Document type: Article
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Colonic Neoplasms / DNA Copy Number Variations Type of study: Guideline / Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Cancer Med Year: 2023 Document type: Article