Nabil Ibtehaz

PathFold predicts protein folding pathways from sequence using a deep learning framework combining AlphaFold and diffusion models, showing agreement with experimental data.

Prot-LAMBDA improves protein structure prediction by explicitly incorporating spatial relationships, achieving better performance than ESM2-3B in contact prediction and 3D structure prediction.

Queryome is a deep research system that improves biomedical literature synthesis through iterative reasoning and retrieval, achieving higher accuracy than existing methods.

CryoZeta improves cryo-EM structure modeling by integrating density features with sequence data using a diffusion-based deep learning model, achieving higher accuracy than existing methods.

NuFold predicts RNA tertiary structures using deep learning with a nucleobase center representation to bridge the gap between sequence data and structural insights.

The paper predicts transcriptional activation domain function using graph neural networks, achieving high accuracy and outperforming existing methods.

GO2Sum generates human-readable protein function summaries from GO terms using a fine-tuned T5 model, outperforming the base T5 model in creating function descriptions for UniProt entries.

The paper introduces Domain-PFP, a method for protein function prediction using function-aware domain embeddings, which outperforms existing methods and large-scale protein language models in GO prediction tasks.

NORTH is a Naive Bayes-based algorithm for orthologous gene clustering that achieves high accuracy and scalability across diverse organisms and large datasets.