A Bayesian machine learning approach for drug target identification using diverse data types
oleh: Neel S. Madhukar, Prashant K. Khade, Linda Huang, Kaitlyn Gayvert, Giuseppe Galletti, Martin Stogniew, Joshua E. Allen, Paraskevi Giannakakou, Olivier Elemento
| Format: | Article |
|---|---|
| Diterbitkan: | Nature Portfolio 2019-11-01 |
Deskripsi
Drug target identification is a crucial step in drug development. Here, the authors introduce a Bayesian machine learning framework that integrates multiple data types to predict the targets of small molecules, enabling identification of a new set of microtubule inhibitors and the target of the anti-cancer molecule ONC201.