Network-Based Drug-Target Interaction Prediction with Probabilistic Soft Logic

Drug–target interaction prediction through domain-tuned network-based inference

MOTIVATION The identification of drug-target interaction (DTI) represents a costly and time-consuming step in drug discovery and design. Computational methods capable of predicting reliable DTI play an important role in the field. Recently, recommendation methods relying on network-based inference (NBI) have been proposed. However, such approaches implement naive topology-based inference and do...

Exploring Drug-Target Interaction Network with NodeXL

I was preparing an extended version of our paper on Probabilistic Soft Logic (PSL) for drug-target interaction prediction [1] that was invited for submission to IEEE-TCBB journal, and it approximately coincided with the NodeXL homework. I planned to use NodeXL to find interesting observations in the dataset we used for our experimental analysis and add it to the journal submission. The followin...

Similarity based learning method for drug target interaction prediction

Biases of network drug–target interaction data

Network based prediction of interaction between drug compounds and target proteins is a core step in the drug discovery process. The availability of drug–target interaction data has boosted the development of machine learning methods for the in silico prediction of drug– target interactions. In this paper we focus on the crucial issue of data bias. We show that four popular datasets contain a b...

Biases of Drug-Target Interaction Network Data

Network based prediction of interaction between drug compounds and target proteins is a core step in the drug discovery process. The availability of drug–target interaction data has boosted the development of machine learning methods for the in silico prediction of drug– target interactions. In this paper we focus on the crucial issue of data bias. We show that four popular datasets contain a b...