A probabilistic approach for collective similarity-based drug-drug interaction prediction.

Motivation: As concurrent use of multiple medications becomes ubiquitous among patients, it is crucial to characterize both adverse and synergistic interactions between drugs. Statistical methods for prediction of putative drug-drug interactions (DDIs) can guide in vitro testing and cut down significant cost and effort. With the abundance of experimental data characterizing drugs and their associated targets, such methods must effectively fuse multiple sources of information and perform inference over the network of drugs.

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

Drug-target interaction studies are important because they can predict drugs' unexpected therapeutic or adverse side effects. In silico predictions of potential interactions are valuable and can focus effort on in vitro experiments. We propose a prediction framework that represents the problem using a bipartite graph of drug-target interactions augmented with drug-drug and target-target similarity measures and makes predictions using probabilistic soft logic (PSL).

Bias and Stability of Single Variable Classifiers for Feature Ranking and Selection.

Feature rankings are often used for supervised dimension reduction especially when discriminating power of each feature is of interest, dimensionality of dataset is extremely high, or computational power is limited to perform more complicated methods. In practice, it is recommended to start dimension reduction via simple methods such as feature rankings before applying more complex approaches. Single Variable Classifier (SVC) ranking is a feature ranking based on the predictive performance of a classifier built using only a single feature.

Confident Surgical Decision Making in Temporal Lobe Epilepsy by Heterogeneous Classifier Ensembles.

In medical domains with low tolerance for invalid predictions, classification confidence is highly important and traditional performance measures such as overall accuracy cannot provide adequate insight into classifications reliability. In this paper, a confident-prediction rate (CPR) which measures the upper limit of confident predictions has been proposed based on receiver operating characteristic (ROC) curves. It has been shown that heterogeneous ensemble of classifiers improves this measure.