Prediction of Drug-target Binding Affinity by An Ensemble Lear ning System with Network Fusion Information

Abstract

Background: Verifying interactions between drugs and targets is key to discover new drugs. Many computational methods have been developed to predict drug-target interactions and performed successfully, but challenges still exist in the field. Objective: We try to develop a machine learning method to predict drug-target affinity, which can determine the strength of the binding relationship between drug and target. Method: This paper proposes an integrated machine learning system for drug-target binding affinity prediction based on network fusion. First, multiple similarity networks representing drugs or targets are calculated. Second, multiple networks representing drugs (targets) are fused separately. Finally, the characteristic information of splicing drugs and targets was used for model construction and training. By integrating multiple similarity networks, the model fully embodies the complementarity of network information, and the most complete features of information can be obtained after the redundancy is removed. Results: Experimental results showed that our model obtained good results for DTI binding affinity. Conclusion: It is still challenging to predict drug-target affinity. This paper proposes to use an integrated system of fusion network information for addressing the issue, and the proposed method performs well, which can provide a certain data basis for the subsequent work. Website: https://www.dlearningapp.com/web/inmpba.htm

MILNP: Plant lncRNA–miRNA Interaction Prediction Based on Improved Linear Neighborhood Similarity and Label Propagation

“…As computer technology advances rapidly, numerous methods are employed to study miRNAs, lncRNAs, and proteins, as well as their interactions (Fu et al, 2019(Fu et al, , 2020Cai et al, 2020aCai et al, ,b, 2021Dai et al, 2021;Li P. et al, 2021;Liu et al, 2021;Rahaman et al, 2021;Song et al, 2021;Tan et al, 2021;Zhang C. L. et al, 2021;Zhang et al, 2022). With regard to miRNAs, a miRNA that is positively selected during human evolution is identified to regulate energy expenditure, and the relevance of this positively selected locus to metabolic disorders may explain the link between this locus and metabolic diseases (Stower, 2020).…”