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Drug discovery relies on predicting drug-target interaction (DTI), which is an important challenging task. The purpose of DTI is to identify the interaction between drug chemical compounds 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 proposed 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 predictive performance. This feature selection algorithm removes irrelevant features to obtain optimal 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. The datasets and source code are available at: https://github.com/Khojasteh-hb/SRX-DTI. [ABSTRACT FROM AUTHOR]

The reconstruction of the topology of gene regulatory networks (GRNs) using high throughput genomic data such as microarray gene expression data is an important problem in systems biology. The main challenge in gene expression data is the high number of genes and low number of samples; also the data are often impregnated with noise. In this paper, in dealing with the noisy data, Kalman filter based method that has the ability to use prior knowledge on learning the network was used. In the proposed method namely (KFLR), in the first phase by using mutual information, the noisy regulations with low correlations were removed. The proposed method utilized a new closed form solution to compute the posterior probabilities of the edges from regulators to the target gene within a hybrid framework of Bayesian model averaging and linear regression methods. In order to show the efficiency, the proposed method was compared with several well know methods. The results of the evaluation indicate that the inference accuracy was improved by the proposed method which also demonstrated better regulatory relations with the noisy data. [ABSTRACT FROM AUTHOR]