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DTiGEMS+: drug-target interaction prediction using graph embedding, graph mining, and similarity-based techniques.
Thafar, Maha A; Olayan, Rawan S; Ashoor, Haitham; Albaradei, Somayah; Bajic, Vladimir B; Gao, Xin; Gojobori, Takashi; Essack, Magbubah.
Affiliation
  • Thafar MA; Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.
  • Olayan RS; Collage of Computers and Information Technology, Taif University, Taif, Kingdom of Saudi Arabia.
  • Ashoor H; Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.
  • Albaradei S; The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
  • Bajic VB; Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.
  • Gao X; The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA.
  • Gojobori T; Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia.
  • Essack M; Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia.
J Cheminform ; 12(1): 44, 2020 Jun 29.
Article de En | MEDLINE | ID: mdl-33431036
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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Type d'étude: Prognostic_studies / Risk_factors_studies Langue: En Journal: J Cheminform Année: 2020 Type de document: Article Pays de publication: Royaume-Uni
Texte intégral
Ajouter à My VHL
Imprimer
XML
PubMed Links
Recherche sur Google

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Type d'étude: Prognostic_studies / Risk_factors_studies Langue: En Journal: J Cheminform Année: 2020 Type de document: Article Pays de publication: Royaume-Uni