Mohsen Naghipourfar

State, a set-based transformer trained on over 100 million perturbed cells, predicts perturbation responses in new cellular contexts better than mean, linear and deep learning baselines.

KODA uses LLMs and knowledge graphs to identify antimicrobial drug targets in the gut microbiome by analyzing metabolic pathways and microbial interactions.

A 40B-parameter DNA model trained on 9.3 trillion tokens scores coding and noncoding variants zero-shot, exposes interpretable sequence features, and writes chromosome-scale sequences.

The paper introduces cdsFM, a suite of codon-resolution large language models that predict the functional consequences of synonymous codon choices and identify pathogenic variants.

The paper introduces Mach-1, an RNA foundation model that predicts transcriptome architecture from DNA sequence, validated through experimental perturbations and CRISPR editing.

This study introduces ExoGRU, a machine learning model that predicts small RNA secretion from sequence, identifies key RNA-binding proteins, and develops exoCLIP to map RNA interactomes in extracellular vesicles.

This paper introduces scArches, a deep learning method for mapping query datasets onto reference single-cell atlases using transfer learning, effectively reducing batch effects and preserving biological information with fewer parameters.