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Reciprocal perspective as a super learner improves drug-target interaction prediction (MUSDTI).
Dick, Kevin; Kyrollos, Daniel G; Cosoreanu, Eric D; Dooley, Joseph; Fryer, Joshua S; Gordon, Shaun M; Kharbanda, Nikhil; Klamrowski, Martin; LaCasse, Patrick N L; Leung, Thomas F; Nasir, Muneeb A; Qiu, Chang; Robinson, Aisha S; Shao, Derek; Siromahov, Boyan R; Starlight, Evening; Tran, Christophe; Wang, Christopher; Yang, Yu-Kai; Green, James R.
  • Dick K; Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada. kevin.dick@carleton.ca.
  • Kyrollos DG; Institute of Data Science, Carleton University, Ottawa, ON, Canada. kevin.dick@carleton.ca.
  • Cosoreanu ED; Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
  • Dooley J; Institute of Data Science, Carleton University, Ottawa, ON, Canada.
  • Fryer JS; Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
  • Gordon SM; Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
  • Kharbanda N; Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
  • Klamrowski M; Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
  • LaCasse PNL; Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
  • Leung TF; Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
  • Nasir MA; Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
  • Qiu C; Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
  • Robinson AS; Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
  • Shao D; Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
  • Siromahov BR; Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
  • Starlight E; Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
  • Tran C; Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
  • Wang C; Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
  • Yang YK; Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
  • Green JR; Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, Canada.
Sci Rep ; 12(1): 13237, 2022 08 02.
Article in English | MEDLINE | ID: covidwho-2016819
ABSTRACT
The identification of novel drug-target interactions (DTI) is critical to drug discovery and drug repurposing to address contemporary medical and public health challenges presented by emergent diseases. Historically, computational methods have framed DTI prediction as a binary classification problem (indicating whether or not a drug physically interacts with a given protein target); however, framing the problem instead as a regression-based prediction of the physiochemical binding affinity is more meaningful. With growing databases of experimentally derived drug-target interactions (e.g. Davis, Binding-DB, and Kiba), deep learning-based DTI predictors can be effectively leveraged to achieve state-of-the-art (SOTA) performance. In this work, we formulated a DTI competition as part of the coursework for a senior undergraduate machine learning course and challenged students to generate component DTI models that might surpass SOTA models and effectively combine these component models as part of a meta-model using the Reciprocal Perspective (RP) multi-view learning framework. Following 6 weeks of concerted effort, 28 student-produced component deep-learning DTI models were leveraged in this work to produce a new SOTA RP-DTI model, denoted the Meta Undergraduate Student DTI (MUSDTI) model. Through a series of experiments we demonstrate that (1) RP can considerably improve SOTA DTI prediction, (2) our new double-cold experimental design is more appropriate for emergent DTI challenges, (3) that our novel MUSDTI meta-model outperforms SOTA models, (4) that RP can improve upon individual models as an ensembling method, and finally, (5) RP can be utilized for low computation transfer learning. This work introduces a number of important revelations for the field of DTI prediction and sequence-based, pairwise prediction in general.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Drug Discovery / Drug Development Type of study: Experimental Studies / Prognostic study Limits: Humans Language: English Journal: Sci Rep Year: 2022 Document Type: Article Affiliation country: S41598-022-16493-9

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Drug Discovery / Drug Development Type of study: Experimental Studies / Prognostic study Limits: Humans Language: English Journal: Sci Rep Year: 2022 Document Type: Article Affiliation country: S41598-022-16493-9