Affiliation:
1. Department of Computer Science and Engineering, Anna University, Chennai-600025, India
2. Department of Decision
and Information Sciences, Oakland University, Rochester, MI 48309, USA
3. Center for Data Science and Big Data Analytics,
Oakland University, Rochester, MI 48309, USA
Abstract
Background:
Drug-Target Interactions (DTIs) are used to suggest new medications for diseases
or reuse existing drugs to treat other diseases since experimental procedures take years to complete,
and FDA (Food and Drug Administration) permission is necessary for drugs to be made available
in the market.
Objective:
Computational methods are favoured over wet-lab experiments in drug analysis, considering
that the process is tedious, time-consuming, and costly. The interactions between drug targets are computationally
identified, paving the way for unknown drug-target interactions for numerous diseases unknown
to researchers.
Methods:
This paper presents a Chronological Order-based Wrapper Technique for Drug-Target Interaction
prediction (CO-WT DTI) to discover novel DTI. In our proposed approach, drug features, as
well as protein features, are obtained by three feature extraction techniques while dimensionality reduction
is implemented to remove unfavourable features. The imbalance issue is taken care of by balancing
methods while the performance of the proposed approach is validated on benchmark datasets.
Results:
The proposed approach has been validated using four broadly used benchmark datasets, namely,
GPCR (G protein-coupled receptors), enzymes, nuclear receptors, and ion channels. Our experimental
results outperform other state-of-the-art methods based on the AUC (area under the Receiver
Operating Characteristic (ROC) curve) metric, and Leave-One-Out Cross-Validation (LOOCV) is used
to evaluate the prediction performance of the proposed approach.
Conclusion:
The performance of feature extraction, balancing methods, dimensionality reduction, and
classifier suggests ways to contribute data to the development of new drugs. It is anticipated that our
model will help refine ensuing explorations, especially in the drug-target interaction domain.
Publisher
Bentham Science Publishers Ltd.
Subject
Computational Mathematics,Genetics,Molecular Biology,Biochemistry
Cited by
1 articles.
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