Anup Kumar

PGRanker improves Mendelian gene prioritization by integrating phenotype data with gene networks using graph learning, achieving higher recall and better ranking of causal genes.

Hi-cGAN predicts Hi-C interaction matrices using conditional generative adversarial networks, achieving better performance than existing methods at certain resolutions.

The paper introduces GraphMeX-plain, a graph neural network with explainable AI to prioritize disease-specific CpG sites as potential biomarkers in breast cancer and acute myeloid leukemia.

This paper develops a Transformer-based tool recommender system for Galaxy, achieving faster convergence and better generalization than a previous RNN-based system.

The paper presents a docker-based Jupyterlab infrastructure in Galaxy for AI development, enabling remote training and management of AI projects with GPU support and version control.

The paper uses a generative neural network to model the evolution of SARS-CoV-2 spike protein sequences between adjacent clades, showing high correlation with true evolutionary patterns.

The paper introduces Galaxy-ML, a web-based platform for reproducible and scalable supervised machine learning in biomedicine, extending the Galaxy computational workbench.

The paper develops a deep learning model to recommend Galaxy tools based on workflow analysis, achieving 97% accuracy in tool prediction.