Anowarul Kabir

The paper models co-occurrence patterns of obstetric and non-obstetric diagnoses during pregnancy using graph neural networks and identifies clinically relevant comorbidity associations.

LoMuS improves protein stability prediction using a multimodal deep learning model that combines physicochemical descriptors with low-rank adapted embeddings from a protein language model.

The paper develops a deep surrogate model to generate DNA breathing features efficiently, improving transcription factor binding predictions.

The paper introduces JAX-EPBD, a high-throughput framework for simulating DNA breathing dynamics, showing 30x speedup and improved predictions of transcription factor binding using SELEX datasets.

The paper improves TF binding site prediction by integrating DNA biophysical properties with sequence data using a multi-modal deep learning model, achieving better performance in both in vivo and in vitro datasets.

The paper introduces pyDNA-EPBD, a software tool for simulating DNA breathing dynamics using the EPBD model, and demonstrates its ability to calculate DNA bubble probabilities and mutation effects.

This paper introduces GOProFormer, a transformer-based method for Gene Ontology protein function prediction, showing superior performance over existing methods and proposing a new benchmark dataset.