Your browser doesn't support javascript.
loading
A new precision medicine initiative at the dawn of exascale computing.
Nussinov, Ruth; Jang, Hyunbum; Nir, Guy; Tsai, Chung-Jung; Cheng, Feixiong.
Afiliação
  • Nussinov R; Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD, 21702, USA. NussinoR@mail.nih.gov.
  • Jang H; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, 69978, Israel. NussinoR@mail.nih.gov.
  • Nir G; Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD, 21702, USA.
  • Tsai CJ; Department of Genetics, Harvard Medical School, Boston, MA, 02115, USA.
  • Cheng F; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA.
Signal Transduct Target Ther ; 6(1): 3, 2021 01 06.
Article em En | MEDLINE | ID: mdl-33402669
Which signaling pathway and protein to select to mitigate the patient's expected drug resistance? The number of possibilities facing the physician is massive, and the drug combination should fit the patient status. Here, we briefly review current approaches and data and map an innovative patient-specific strategy to forecast drug resistance targets that centers on parallel (or redundant) proliferation pathways in specialized cells. It considers the availability of each protein in each pathway in the specific cell, its activating mutations, and the chromatin accessibility of its encoding gene. The construction of the resulting Proliferation Pathway Network Atlas will harness the emerging exascale computing and advanced artificial intelligence (AI) methods for therapeutic development. Merging the resulting set of targets, pathways, and proteins, with current strategies will augment the choice for the attending physicians to thwart resistance.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Sistemas Computacionais / Inteligência Artificial / Medicina de Precisão / Modelos Biológicos Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Sistemas Computacionais / Inteligência Artificial / Medicina de Precisão / Modelos Biológicos Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article