“School of Biological Sciences”
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| Paper IPM / Biological Sciences / 17305 |
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| Abstract: | |||||||
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rug discovery relies on predicting drug-target interaction (DTI), which is an important chal-
lenging task. The purpose of DTI is to identify the interaction between drug chemical com-
pounds and protein targets. Traditional wet lab experiments are time-consuming and
expensive, thatâs why in recent years, the use of computational methods based on machine
learning has attracted the attention of many researchers. Actually, a dry lab environment
focusing more on computational methods of interaction prediction can be helpful in limiting
search space for wet lab experiments. In this paper, a novel multi-stage approach for DTI is
proposed that called SRX-DTI. In the first stage, combination of various descriptors from
protein sequences, and a FP2 fingerprint that is encoded from drug are extracted as feature
vectors. A major challenge in this application is the imbalanced data due to the lack of
known interactions, in this regard, in the second stage, the One-SVM-US technique is pro-
posed to deal with this problem. Next, the FFS-RF algorithm, a forward feature selection
algorithm, coupled with a random forest (RF) classifier is developed to maximize the predic-
tive performance. This feature selection algorithm removes irrelevant features to obtain opti-
mal features. Finally, balanced dataset with optimal features is given to the XGBoost
classifier to identify DTIs. The experimental results demonstrate that our proposed
approach SRX-DTI achieves higher performance than other existing methods in predicting
DTIs.
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