Behnam Yousefi

BioChemAIgent: An AI-driven Protein Modeling and Docking Framework for Structure-Based Drug Discovery

BioChemAIgent provides an AI-driven framework for protein modeling and docking in drug discovery, integrating multiple tools into a unified interface.

SCALE: Unsupervised Multi-Scale Domain Identification in Spatial Omics Data

SCALE identifies multi-scale functional domains in spatial transcriptomics data using neural graph learning and entropy-based search, achieving state-of-the-art performance on simulated and biological datasets.

Spatio-temporal interaction of immune and renal cells determines glomerular crescent formation in autoimmune kidney disease

This study uses spatial transcriptomics to show that immune and renal cell interactions drive glomerular crescent formation in RPGN through PDGF and TGF-β signaling to PECs.

PCA-based spatial domain identification with state-of-the-art performance

This paper introduces NichePCA, a PCA-based method for spatial domain identification that performs competitively with ten state-of-the-art methods across six spatial transcriptomic datasets.

Consensus Clustering for Robust Bioinformatics Analysis

This paper reviews consensus clustering methods, their stability scores, and applications to gene expression data, providing open-source R implementations.