Lixiang Hong

TL;DR: A new nonlinear end‐to‐end learning model that integrates diverse information from heterogeneous network data and automatically learns topology‐preserving representations of drugs and targets to facilitate DTI prediction is developed, suggesting that NeoDTI can offer a powerful and robust tool for drug development and drug repositioning.

TL;DR: The in silico screening followed by wet-lab validation indicated that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor, CVL218, currently in Phase I clinical trial, may be repurposed to treat COVID-19 and proposed several possible mechanisms to explain the antiviral activities of PARP1 inhibitors against SARS-CoV-2.

TL;DR: Wang et al. as discussed by the authors developed an integrative drug repositioning framework, which fully takes advantage of machine learning and statistical analysis approaches to systematically integrate and mine large-scale knowledge graph, literature and transcriptome data to discover the potential drug candidates against SARS-CoV-2.

TL;DR: A novel machine learning framework, named BERE, for automatically extracting biomedical relations from large-scale literature repositories, which uses a hybrid encoding network to better represent each sentence from both semantic and syntactic aspects, and employs a feature aggregation network to make predictions after considering all relevant statements.

TL;DR: A highly interpretable computational framework, called MASS, based on a multi-task curriculum learning strategy to capture m6A features across multiple species simultaneously is proposed, demonstrating the superior performances of MASS when compared to the state-of-the-art prediction methods.