Siqi Liang

Structurally-informed human interactome reveals proteome-wide perturbations by disease mutations

The paper introduces PIONEER, a deep learning framework for predicting protein-protein interfaces, and shows disease mutations are enriched in these interfaces, identifying oncoPPIs linked to cancer survival and drug responses.

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.

PIVOTAL: Prioritizing variants of uncertain significance with spatial genomic patterns in the 3D proteome

PIVOTAL prioritizes variants of uncertain significance using 3D proteome spatial patterns, predicting pathogenicity for 140,000 VUS and providing a web tool for analysis.

Leveraging genetic interaction for adverse drug-drug interaction prediction

This paper predicts adverse drug-drug interactions using genetic interaction features and a gradient boosting classifier, identifying 432 novel interactions and providing mechanistic insights.

Revealing new therapeutic opportunities through drug target prediction via class imbalance-tolerant machine learning

This study predicts drug targets using a class imbalance-tolerant machine learning framework, identifying new drug-target associations for potential cancer treatment.

Interactome INSIDER: A Multi-Scale Structural Interactome Browser For Genomic Studies

The paper introduces Interactome INSIDER, a browser for genomic studies that provides predicted protein interaction interfaces and enables analysis of mutations in these interfaces.