PMID- 32154410
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20200928
IS  - 2405-8440 (Print)
IS  - 2405-8440 (Electronic)
IS  - 2405-8440 (Linking)
VI  - 6
IP  - 3
DP  - 2020 Mar
TI  - FRnet-DTI: Deep convolutional neural network for drug-target interaction 
      prediction.
PG  - e03444
LID - 10.1016/j.heliyon.2020.e03444 [doi]
LID - e03444
AB  - 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/.
CI  - (c) 2020 Published by Elsevier Ltd.
FAU - Rayhan, Farshid
AU  - Rayhan F
AD  - Department of Computer Science and Engineering, United International University, 
      Plot 2, United City, Madani Avenue, Satarkul, Badda, Dhaka-1212, Bangladesh.
FAU - Ahmed, Sajid
AU  - Ahmed S
AD  - Department of Computer Science and Engineering, United International University, 
      Plot 2, United City, Madani Avenue, Satarkul, Badda, Dhaka-1212, Bangladesh.
FAU - Mousavian, Zaynab
AU  - Mousavian Z
AD  - School of Mathematics, Statistics, and Computer Science, College of Science, 
      University of Tehran, Tehran, Iran.
FAU - Farid, Dewan Md
AU  - Farid DM
AD  - Department of Computer Science and Engineering, United International University, 
      Plot 2, United City, Madani Avenue, Satarkul, Badda, Dhaka-1212, Bangladesh.
FAU - Shatabda, Swakkhar
AU  - Shatabda S
AD  - Department of Computer Science and Engineering, United International University, 
      Plot 2, United City, Madani Avenue, Satarkul, Badda, Dhaka-1212, Bangladesh.
LA  - eng
PT  - Journal Article
DEP - 20200302
PL  - England
TA  - Heliyon
JT  - Heliyon
JID - 101672560
PMC - PMC7052404
OTO - NOTNLM
OT  - Bioinformatics
OT  - Class imbalance
OT  - Classification
OT  - Computer Science
OT  - Drug-Target
OT  - Ensemble classifier
OT  - Feature engineering
EDAT- 2020/03/11 06:00
MHDA- 2020/03/11 06:01
PMCR- 2020/03/02
CRDT- 2020/03/11 06:00
PHST- 2018/12/10 00:00 [received]
PHST- 2019/06/16 00:00 [revised]
PHST- 2020/02/14 00:00 [accepted]
PHST- 2020/03/11 06:00 [entrez]
PHST- 2020/03/11 06:00 [pubmed]
PHST- 2020/03/11 06:01 [medline]
PHST- 2020/03/02 00:00 [pmc-release]
AID - S2405-8440(20)30289-9 [pii]
AID - e03444 [pii]
AID - 10.1016/j.heliyon.2020.e03444 [doi]
PST - epublish
SO  - Heliyon. 2020 Mar 2;6(3):e03444. doi: 10.1016/j.heliyon.2020.e03444. eCollection 
      2020 Mar.