Mehdi Mirzaie

This paper introduces an energy profile-based method for rapid structural and evolutionary protein analysis, showing it can distinguish proteins across species and correlate with network-based approaches.

This study uses multipartite network models to predict effective drug combinations and patient subclasses in acute myeloid leukemia using drug response data.

NIMAA is an R package for nominal data mining that provides tools for graph construction, clustering, and visualization, applied to a biological dataset.

This study evaluates whether gene expression profiles can reliably infer signaling network activity and finds they are not more coherent with causal relationships than random data.

This study identifies key genes involved in HAM/TSP pathogenesis through high-throughput data integration and meta-analysis, revealing differential expression of STAT1, TAP1, and PSMB8.

IMMAN is an R/Bioconductor package that reconstructs interolog protein networks by integrating PPINs and enables conservation analysis across species for protein function prediction.

CINNA compares centrality measures to identify the most informative nodes in networks using dimensionality reduction.

The paper systematically evaluates centrality measures in yeast protein-protein interaction networks to identify which measures best predict influential proteins.

The paper designs a drug combination to inhibit EMT in a 3D microenvironment by targeting histone deacetylases, SRC, and IKBK, showing effectiveness in a microfluidic model.

This paper modifies and mathematically models the Central Dogma of molecular biology using Boolean networks to show increased complexity and robustness in biological systems.

This study identifies signaling pathways affected by rabies virus using systems biomedicine approaches, revealing potential drug targets through network analysis of gene expression data.