Motivation: Finding computationally drug-target interactions (DTIs) is a convenient strategy to identify new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer a high false-positive prediction rate. Results: We developed DDR, a novel method that improves the DTI prediction accuracy. DDR is based on the use of a heterogeneous graph that contains known DTIs with multiple similarities between drugs and multiple similarities between target proteins. DDR applies non-linear similarity fusion...

Abstract: Discriminating the causative disease variant(s) for individuals with inherited or de novo mutations present one of the main challenges faced by the clinical genetics community today. Computational approaches for variant prioritization include machine learning methods utilizing a large number of features, including molecular information, interaction networks, or phenotypes. Here, we demonstrate the PhenomeNET Variant Predictor (PVP) system that exploits semantic technologies and automated reasoning over genotype-phenotype relations to filter and prioritize variants in whole...

The supplementary material for paper "Rui Li, Wolfgang Heidrich, Hierarchical and View-invariant Light Field Segmentation by Maximizing Entropy Rate on 4D Ray Graphs. In SIGGRAPH Asia, 2019"

The GenRot program takes a typical Gaussian .gjf file (connectivity written after XYZ) as input, and generates new XYZ data based on inputs from the user. After opening the file in the program, the user clicks on 4 atoms to define a dihedral angle and determines the degree of rotation they wish for that angle. This is repeated until all the dihedral angles that the user wants to rotate have been defined. The program will...