J
Jeffrey N. Law
Researcher at Virginia Tech
Publications - 19
Citations - 593
Jeffrey N. Law is an academic researcher from Virginia Tech. The author has contributed to research in topics: Computer science & Interaction network. The author has an hindex of 6, co-authored 15 publications receiving 232 citations.
Papers
More filters
Journal ArticleDOI
Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data.
TL;DR: A systematic evaluation of state-of-the-art algorithms for inferring gene regulatory networks from single-cell transcriptional data finds heterogeneous performance and suggests recommendations to users.
Posted ContentDOI
Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data
TL;DR: It is suggested that new ideas for avoiding the prediction of indirect interactions appear to be necessary to improve the accuracy of GRN inference algorithms for single cell gene expression data.
Journal ArticleDOI
The PathLinker app: Connect the dots in protein interaction networks.
TL;DR: The app presented here makes the PathLinker functionality available to Cytoscape users and presents an example where the method was used to compute and analyze the network of interactions connecting proteins that are perturbed by the drug lovastatin.
Journal ArticleDOI
Automating the PathLinker app for Cytoscape
TL;DR: This paper describes how the Pathlinker app is automated to use the CyRest infrastructure and how users can incorporate PathLinker into their software pipelines.
Posted Content
Identifying Human Interactors of SARS-CoV-2 Proteins and Drug Targets for COVID-19 using Network-Based Label Propagation
Jeffrey N. Law,Nure Tasnina,Meghana Kshirsagar,Judith Klein-Seetharaman,Mark Crovella,Padmavathy Rajagopalan,Simon Kasif,T. M. Murali +7 more
TL;DR: A genome-scale, systems-level computational approach to prioritize drug targets based on their potential to regulate host-virus interactions or their downstream signaling targets is presented, and it is demonstrated that these techniques can predict human-SARS-CoV-2 protein interactors with high accuracy.