Drug Target Interaction Prediction Based On Logistic Regression

Drug discovery and drug reposition are time-consuming and expensive process all the time,and identifying drug-target interactions that considered from the molecular level is an effective method for drug discovery and drug reposition.However,finding interactions between drugs and targets is difficult for the difficult of feature design and the complexity of computing.Recently,many statistical models effectively inferred novel drug-target interactions,but they suffered some limitations.Firstly,data sets used only contain true-positive samples,and experimentally validated negative samples are not available,because database about drug target interactions in internet only contain the certain interactions of drugs and targets,while the opposite interactions between drug and target does' t exist in database.Secondly,the problems of expressing and selecting drug features and target features.It is mainly to consider how to select the appropriate features and express a drug and a target,and how to combine features of drug target pair.In this thesis,we discussed feature processing of drugs and targets,and we proposed a concept of network features.To predict interactions of drugs and targets and solve the problems of samples without negative samples,we improved a linear logistic regression and proposed two dimensional logistic regression model.the main work described as follows:1)In order to express the feature of drug target pair more accurately,we put forward the concept of network features.First,we obtain the structure features of drugs and targets.We use information gain principal component analysis(IGPCA)for drug structure features and target structure features to perform independence.Then,taking into account the information of drug target interaction,we extract drug network features and target network features from the matrix of drug target interaction.Finally,we verify the effectiveness of the feature processing method by several group of five-fold cross validation.The experimental results show that the principal component analysis method can achieve good results under various classifiers,and the extracted network features are effective under most classifiers.2)In order to solve the problem of negative sample and feature expression problems,we propose a new model——two dimensional logistic regression model(TwoDLLR),which improves the traditional logistic regression model.It can be well adapted to the case of the drug target relationship prediction,and the model can predict interactions without negative samples.Finally,we verify the effectiveness of our method by several group of 5-fold cross validation experiments.The experimental results show that our method can achieve good results under different classification evaluation index,and results show that TwoDLLR method is able to discover most of interactions of new drug and interactions of new target.