Sagar Gupta

GANGE is a deep-learning system that generates accurate genomic sequences from low-coverage, error-prone reads, reducing sequencing costs and enabling regulomics research without genome data.

DMRU is a generative deep-learning model that identifies condition-specific DNA methylation in plants with high accuracy across species and conditions.

PTF-Vac discovers plant transcription factor binding sites using a generative deep learning model that outperforms existing methods and is independent of species and predefined motifs.

The paper uses deep learning to identify critical cytosines in gene regulation across plant species by analyzing methylation and expression data.

DeepPlnc is a deep learning system for plant lncRNA discovery that outperforms existing tools with high accuracy and robustness to sequence ambiguity.

The paper introduces miWords, a transformer-based deep learning method for identifying pre-miRNA regions in plant genomes, achieving high accuracy across multiple species.

RBPSpot identifies RBP binding sites using contextual sequence features and a DNN, achieving ~90% accuracy and outperforming existing tools across multiple datasets.