FRnet-DTI: Deep convolutional neural network for drug-target interaction prediction. Issue 3 (March 2020)
- Record Type:
- Journal Article
- Title:
- FRnet-DTI: Deep convolutional neural network for drug-target interaction prediction. Issue 3 (March 2020)
- Main Title:
- FRnet-DTI: Deep convolutional neural network for drug-target interaction prediction
- Authors:
- Rayhan, Farshid
Ahmed, Sajid
Mousavian, Zaynab
Farid, Dewan Md
Shatabda, Swakkhar - Abstract:
- Abstract: The task of drug-target interaction prediction holds significant importance in pharmacology and therapeutic drug design. In this paper, we present FRnet-DTI, an auto-encoder based feature manipulation and a convolutional neural network based classifier for drug target interaction prediction. Two convolutional neural networks are proposed: FRnet-Encode and FRnet-Predict. Here, one model is used for feature manipulation and the other one for classification. Using the first method FRnet-Encode, we generate 4096 features for each of the instances in each of the datasets and use the second method, FRnet-Predict, to identify interaction probability employing those features. We have tested our method on four gold standard datasets extensively used by other researchers. Experimental results shows that our method significantly improves over the state-of-the-art method on three out of four drug-target interaction gold standard datasets on both area under curve for Receiver Operating Characteristic (auROC) and area under Precision Recall curve (auPR) metric. We also introduce twenty new potential drug-target pairs for interaction based on high prediction scores. The source codes and implementation details of our methods are available from https://github.com/farshidrayhanuiu/FRnet-DTI/ and also readily available to use as an web application from http://farshidrayhan.pythonanywhere.com/FRnet-DTI/ . Abstract : Bioinformatics; Computer Science; Drug-Target; Class imbalance;Abstract: The task of drug-target interaction prediction holds significant importance in pharmacology and therapeutic drug design. In this paper, we present FRnet-DTI, an auto-encoder based feature manipulation and a convolutional neural network based classifier for drug target interaction prediction. Two convolutional neural networks are proposed: FRnet-Encode and FRnet-Predict. Here, one model is used for feature manipulation and the other one for classification. Using the first method FRnet-Encode, we generate 4096 features for each of the instances in each of the datasets and use the second method, FRnet-Predict, to identify interaction probability employing those features. We have tested our method on four gold standard datasets extensively used by other researchers. Experimental results shows that our method significantly improves over the state-of-the-art method on three out of four drug-target interaction gold standard datasets on both area under curve for Receiver Operating Characteristic (auROC) and area under Precision Recall curve (auPR) metric. We also introduce twenty new potential drug-target pairs for interaction based on high prediction scores. The source codes and implementation details of our methods are available from https://github.com/farshidrayhanuiu/FRnet-DTI/ and also readily available to use as an web application from http://farshidrayhan.pythonanywhere.com/FRnet-DTI/ . Abstract : Bioinformatics; Computer Science; Drug-Target; Class imbalance; Feature engineering; Ensemble classifier; Classification … (more)
- Is Part Of:
- Heliyon. Volume 6:Issue 3(2020)
- Journal:
- Heliyon
- Issue:
- Volume 6:Issue 3(2020)
- Issue Display:
- Volume 6, Issue 3 (2020)
- Year:
- 2020
- Volume:
- 6
- Issue:
- 3
- Issue Sort Value:
- 2020-0006-0003-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-03
- Subjects:
- Bioinformatics -- Computer Science -- Drug-Target -- Class imbalance -- Feature engineering -- Ensemble classifier -- Classification
Research -- Periodicals
Medical sciences -- Periodicals
Natural history -- Periodicals
Social sciences -- Periodicals
Earth sciences -- Periodicals
Physical sciences -- Periodicals
507.2 - Journal URLs:
- http://www.sciencedirect.com/science/journal/24058440/ ↗
http://www.sciencedirect.com/ ↗ - DOI:
- 10.1016/j.heliyon.2020.e03444 ↗
- Languages:
- English
- ISSNs:
- 2405-8440
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 13384.xml