Golnaz Taheri

The paper presents a structure-agnostic framework for predicting drug-drug interactions using MeSH semantics and a Drug-Target-Protein knowledge graph, showing improved performance with intermediate MeSH annotation depth.

PhyMapNet infers microbiome networks using a phylogeny-guided Bayesian framework, showing improved robustness and reproducibility compared to existing methods.

The paper introduces DTPPI, a machine learning method that uses a drug-target-protein-protein interaction network to predict drug-drug interactions with improved accuracy.

This study identifies lung cancer-specific driver modules using a clustering-based gene-gene interaction network analysis combined with machine learning.

GenePioneer identifies essential cancer genes and modules across 12 cancer types using a network-based model and provides a Python package for practical application.

This study uses an unsupervised machine learning approach to analyze pathways in COVID-19, identifying key pathways related to SARS-CoV-2 infection.

The paper identifies 18 essential genes for SARS-CoV-2 pathogenesis using unsupervised learning on biological networks and features.