2 Works

Code for: Semantic prioritization of novel causative genomic variants

Razali Rozaimi B. Mahamad, Maxat Kulmanov, Yasmeen Hashish, Vladimir B. Bajic, Eva Goncalves-Serra, Nadia Schoenmakers, Georgios V Gkoutos, Paul N. Schofield, Robert Hoehndorf & Imane Boudellioua
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...

Code for \"DDR: a method to predict drug target interactions using multiple similarities\"

Rawan S. Olayan, Haitham Ashoor & Vladimir B. Bajic
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...

Registration Year

  • 2019

Resource Types

  • Software


  • King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia