I am a PhD researcher in the Department of Bioinformatics at the University of North Bengal, advised by Dr. Chiranjib Sarkar (Computational Systems Biology Lab). My research focuses on bioinformatics and computational biology, specifically deep learning methods for biological sequence-based prediction and generative model development. Experience includes neural network training, transfer learning with pretrained models, and analysis of large-scale biological datasets. Current work includes autoregressive generative modeling of biological sequences, with ongoing exploration of diffusion-based approaches for biological data.

Highlights

Deep-Interact Studio workflow

Deep-Interact Studio

No-code platform to design, train, compare, and interpret deep learning models for biomolecular interaction prediction.

 MoE-Bind architecture

MoE-Bind

Autoregressive, sequence-only protein binder generator combining Multi-head Latent Attention with a sparse Mixture-of-Experts.

Publications

Sarkar, D., & Sarkar, C. (2026). ARACoFusion: Uncertainty-aware calibrated deep learning for protein-protein interaction network prediction in Arabidopsis thaliana. bioRxiv, 2026.05.22.727120.

DOI: https://doi.org/10.64898/2026.05.22.727120

Sarkar, D., & Sarkar, C. (2025). AttnSeq-PPI: Enhancing protein-protein interaction network prediction using transfer learning-driven hybrid attention. Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics, 141102.

DOI: https://doi.org/10.1016/j.bbapap.2025.141102

Package 'EGRNi' — Gene Regulatory Network Inference

Sarkar, C.; Sarkar, D.; Parsad, R.; Mishra, D.

CRAN (R package), 2022

cran

Software

Deep-Interact Studio — Interactive web platform for building, training, and comparing custom deep learning models for protein-protein, drug-target, RNA-protein, and protein-DNA interaction prediction, with integrated interpretability.

AttnSeq-PPI — Sequence-only protein-protein interaction prediction using hybrid attention and transfer learning from pretrained protein language models.

ARACoFusion-PPI — Arabidopsis thaliana-specific PPI prediction and interaction network analysis tool.

2026

  • Jul

    Deep-Interact Studio preprint is out on bioRxiv — a no-code platform for building deep learning models of biomolecular interactions.

  • Jun

    MoE-Bind preprint released — sequence-only protein binder generation with sparse Mixture-of-Experts.

  • May

    ARACoFusion preprint released — uncertainty-aware calibrated deep learning for PPI networks in Arabidopsis thaliana.

  • Apr

    Received a Google TRC award for free access to Cloud TPUs (v4/v5e/v6e).

2025

  • Nov

    AttnSeq-PPI published in Biochimica et Biophysica Acta (BBA) — Proteins and Proteomics.

  • Sep

    Awarded GPU cloud compute (NVIDIA L4) under the IndiaAI Compute Initiative.

  • Mar

    Oral presentation at Anusandhan 2025, Bose Institute, Kolkata.

Education

Ph.D. in Bioinformatics

University of North Bengal, India

Advisor: Dr. Chiranjib Sarkar · Deep learning for PPI network prediction

M.Sc. in Botany (Biochemistry)

University of North Bengal, India

First Class · 77.25%

B.Sc. (Hons.) in Botany

Ananda Chandra College, University of North Bengal

First Class · 63.50%

* expected

Experience

PhD Research Scholar

Dept. of Bioinformatics, University of North Bengal

Deep learning for sequence-based PPI prediction; attention-based hybrid models with pretrained protein language models; deployed web platforms for model inference.

Skills

Programming
Python, PyTorch, Hugging Face Transformers, R (CRAN package development), C (basic)
Deep Learning
Transformers, attention mechanisms, sequence modeling, transfer learning with pretrained language models, fine-tuning (LoRA / QLoRA), LLMs, RAG
Generative AI
Autoregressive sequence generation, diffusion-based approaches, probabilistic sequence modeling
Bioinformatics
Sequence-based protein–protein interaction prediction, protein language models, generative modeling of biological sequences
Scalable Training
GPU-based training, large-scale sequence datasets, parallel training on HPC environments
Data & Viz
NumPy, Pandas, Matplotlib, Seaborn
Deployment & Web
Docker, Flask, FastAPI, Linux server deployment, model-backed web apps, React, Vite, Tailwind CSS, Git

Awards & Qualifications

  • CSIR-NET Junior Research Fellowship (JRF) — Life Sciences · June 2021 · All India Rank 216
  • GATE — Life Sciences (XL) — Qualified · 2022

Grants & Computing Resources

  • IndiaAI Compute Initiative (2025)

    Awarded GPU cloud compute (NVIDIA L4) under the IndiaAI Mission for the project Deep learning-based framework for protein-protein interaction network prediction (Project ID: P1-S2025070964, Sep 2025 - Aug 2026). Funded under CSIR-UGC SRF Fellowship.

  • Google TRC (TPU Research Cloud) Award (2025)

    Granted free access to Google Cloud TPUs (v4, v5e, v6e) for machine learning research.

Talks & Presentations

A Webtool for Transfer Learning Based Protein-Protein Interaction Network Prediction Using Hybrid Attention

Anusandhan 2025 — WILEY Sponsored Oral Presentation (SARANSH)

Bose Institute, Kolkata, India · March 7, 2025 Oral