Shayne D. Wierbowski

A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology

This paper introduces NetFlow3D, a framework that integrates protein structure and interaction networks to identify significant mutation clusters and modules in cancer, revealing biological processes not found by traditional methods.

DeepERA: deep learning enables comprehensive identification of drug-target interactions via embedding of heterogeneous data

DeepERA uses deep learning to predict drug-target interactions by integrating heterogeneous data, identifying 45,603 novel interactions including those related to SARS-CoV-2.

A full-proteome, interaction-specific characterization of mutational hotspots across human cancers

The paper develops a computational framework to analyze mutational hotspots in cancer genomes using a human 3D interactome network, identifying clinically relevant interactions and proteins.

A 3D Structural Interactome to Explore the Impact of Evolutionary Divergence, Population Variation, and Small-molecule Drugs on SARS-CoV-2-Human Protein-Protein Interactions

The paper constructs a 3D structural interactome of SARS-CoV-2-human protein interactions, analyzing evolutionary divergence, population variation, and drug impacts.

Structure-based validation can drastically under-estimate error rate in proteome-wide cross-linking mass spectrometry studies

The paper shows that structure-based validation underestimates error rates in proteome-wide XL-MS and proposes four metrics for better quality assessment.

Transcription imparts architecture, function, and logic to enhancer units

The paper identifies fundamental enhancer units defined by transcription start sites and shows adjacent units operate non-cooperatively.