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Paper   IPM / Biological Sciences / 17305
School of Biological Sciences
  Title:   Improving prediction of drug-target interactions based on fusing multiple features with data balancing and feature selection techniques
1.  Hakimeh Khojasteh
2.  Jamshid Pirgazi
3.  Ali Ghanbari Sorkhi
  Status:   Published
  Journal: PLOS ONE
  Year:  2023
  Supported by:  IPM
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, that’s 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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