Avik Sengupta

Avik Sengupta

PhD Scholar in Computational Biology

IIT Hyderabad, India

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About Me

During my PhD at IIT Hyderabad, I develop computational approaches to colorectal cancer theranostics, working across transcriptomics, genomics, Hi-C and clinical datasets from TCGA and DepMap cohorts. My work spans drug repurposing, prognostic modeling, and multi-omics subtype discovery — published in ACS Omega (2024) and Briefings in Bioinformatics (2025).

I am eager to contribute to postdoctoral research in spatial transcriptomics, single-cell multi-omics, and clinical cancer genomics within collaborative environments.

9+ Publications

ACS Omega, Briefings in Bioinformatics, eLife, Frontiers

HPC Pipelines

SLURM-based end-to-end bioinformatics workflows

Excellence Award

Institute Research Excellence, IIT Hyderabad 2025

PhD Research

Objective I
Drug Repurposing via QSAR & SVM

Built quantitative structure–activity relationship (QSAR) and SVM-based predictive models using DepMap/GDSC2 cell line data to identify repurposable drugs and design novel candidates for colorectal cancer.

Published — ACS Omega, 2024
Objective II
ML-Guided Prognostic Risk Models

Developed a two-step machine learning framework using non-apoptotic regulated cell death pathways combined with transcriptomics and clinical data for prognostic risk stratification of CRC patients.

Published — Briefings in Bioinformatics, 2025
Objective III
Multi-omics Subtype Discovery

Designing deep learning and multi-omics clustering workflows integrating linear and structural omics layers to uncover novel molecular subtypes of colorectal cancer.

Ongoing

Technical Skills

Languages & OS
Python R Bash Linux MySQL PHP
Machine Learning
scikit-learn XGBoost GNNs Autoencoders SHAP Optuna Cox PH QSAR
Bioinformatics
STAR/HISAT2 DESeq2 GSEA VEP/GATK HiC-Pro Juicer Seurat maftools
Databases & HPC
TCGA DepMap cBioPortal GEO GDSC2 SLURM HPC Conda

Publications

First-Author
ACS Omega 2024
Support Vector Machine-Based Prediction Models for Drug Repurposing and Designing Novel Drugs for Colorectal Cancer

Avik Sengupta, S.K. Singh, R. Kumar

DOI Link
Briefings in Bioinformatics 2025
Non-apoptotic Regulated Cell Death Based Prognostic Risk Model for Colorectal Cancer Using a Machine Learning Guided Two-Step Framework

Avik Sengupta, S.S. Kar, R. Kumar

DOI Link
Collaborative Articles

Identification of key hub genes in pancreatic ductal adenocarcinoma

Bhattacharjee K, Sengupta A, Kumar R, Ghosh A — Frontiers in Bioinformatics, 2025

DOI

Artificially inserted strong promoter containing multiple G-quadruplexes induces long-range chromatin modification

Roy SS, Bagri S, ..., Sengupta A, ... Chowdhury S — eLife, 2024

DOI

AMLdb: a comprehensive multi-omics platform for acute myeloid leukemia

Vinod Kumar K, ..., Sengupta A, et al. — Briefings in Functional Genomics, 2024

DOI

Deep learning-based classifier of diffuse large B-cell lymphoma cell-of-origin

Viswanathan A, Kundal K, Sengupta A, et al. — Briefings in Functional Genomics, 2023

DOI

MyeloDB: a multi-omics resource for multiple myeloma

Kumar A, ..., Sengupta A, et al. — Functional & Integrative Genomics, 2024

DOI

AagingBase: a comprehensive database of anti-aging peptides

Roffin K, ..., Sengupta A, et al. — Database, 2024

DOI
Book Chapters

Advancement of in silico tools for stem cell research

Kumar A, ..., Sengupta A, Kumar R — Computational Biology for Stem Cell Research, Academic Press, 2024

Drug Repurposing-1

Korra BT, ..., Sengupta A, Kumar R — Springer Handbook of Chem- and Bioinformatics, Springer, 2025

Get In Touch

Available for postdoctoral opportunities in computational cancer biology, multi-omics integration, and clinical genomics research.

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