Aayah Nounu

TL;DR: It is likely that longer telomeres increase risk for several cancers but reduce risk for some non-neoplastic diseases, including cardiovascular diseases, as well as single nucleotide polymorphisms (SNPs) that are strongly associated with telomere length in the general population.

TL;DR: This work proposes a novel computational approach for predicting drug target proteins based on formulating the problem as a link prediction in knowledge graphs (robust, machine-readable representations of networked knowledge) and proposes a specific knowledge graph embedding model, TriModel, to learn vector representaions for all drugs and targets in the created knowledge graph.

TL;DR: It is shown how knowledge graph embedding models can be a natural fit for representing complex biological knowledge modelled as graphs, and their predictive and analytical capabilities in different biology applications are discussed.

TL;DR: This work introduces a novel computational approach for predicting drug target proteins as a link prediction task on knowledge graphs using knowledge graph embedding (KGE) models to enable scoring drug-target associations.

TL;DR: In this article, the authors combined a proteomic approach with Mendelian randomization (MR) to identify possible new aspirin targets that decrease colorectal cancer (CRC) risk.