Mohammad S. Parsa

This paper introduces MP4, a transformer-based model that translates natural language into functional de novo proteins, achieving high performance in sequence realism, fold quality, and function alignment.

The paper introduces TaxoFormer, a hierarchical transformer model that predicts the full taxonomic lineage of protein sequences using a structured tokenization scheme and achieves accurate lineage prediction on a large dataset.

The paper develops a variant interpretation pipeline for nucleoporin genes by integrating structural context and curated clinical data with weakly supervised pseudo-labeling.

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

SIMBA-GNN predicts microbial abundances and cross-feeding relationships by integrating metabolic simulations with a graph neural network, achieving high performance.

This paper introduces Trias, a generative language model that designs mRNAs with high protein output by learning codon usage rules from natural sequences.