Transcriptomics & Multi-Omics Pipeline
Unified workflow for integrating transcriptomics, proteomics and metabolomics across diseases.
PythonRDESeq2WGCNA
Outcome · Identified shared regulatory modules across conditions.
Hello, I'm
Prasanna Sarmah
Ph.D. researcher and computational biologist at CSIR-NEIST building AI-assisted pipelines for multi-omics, transcriptomics and healthcare analytics.
Computational Biologist
specialized in Multi-Omics & AI.
Best Research
Achievement
10+
SCI
Publications
Publications
5+
Years
Research
About Me
Researcher at the intersection of biology and AI.
Prasanna Sarmah is a Ph.D. researcher specialising in computational biology, transcriptomics, proteomics, RNA-seq analysis, structural bioinformatics, molecular simulations, and machine learning workflows for biological data.
He builds AI-assisted biological pipelines using Python, PyTorch, TensorFlow and HPC/cloud environments, applying them to healthcare analytics, multi-omics integration and life sciences research at CSIR-NEIST, Jorhat.
5+
Years research
10+
Publications
15+
Tools & stacks
Research Interests
Computational Biology
Bioinformatics
Transcriptomics & RNA-seq
Structural Bioinformatics
Machine Learning in Biology
Plant Stress Biology
Education
Academic timeline
2022 — Ongoing
Ph.D. in Biological Sciences
CSIR-North East Institute of Science and Technology (under AcSIR)
2018 — 2020
M.Sc. in Biotechnology & Bioinformatics
North Eastern Hill University, Shillong
2015 — 2018
B.Sc. in Biotechnology
North Eastern Hill University, Shillong
Experience
Where research meets practice
5 Years
Project Associate
CSIR-North East Institute of Science and Technology (CSIR-NEIST) · Jorhat, India
Computational analysis of phytochemicals against major human diseases
Biomarker-driven pathway and network analysis
Integration of omics data with machine learning
Disease resistance and stress signaling analysis
Interdisciplinary biological research collaboration
Biological data analysis and computational workflow development
Skills
A full research stack
From wet lab fundamentals to deep learning frameworks and HPC environments.
Bioinformatics & Computational Biology
TranscriptomicsProteomicsRNA-seq AnalysisStructural BioinformaticsMolecular DockingMolecular DynamicsWGCNACo-expression NetworksMulti-omics IntegrationCheminformaticsPlant Stress Biology
Cell Culture & Bioprocess
Plant Tissue CultureMicrobial Cell CultureBioprocess Engineering
Machine Learning & AI
PyTorchTensorFlowJAXDeep LearningTransformersLSTMRNNRepresentation LearningPredictive ModelingClassification & Regression
Programming & Data Science
PythonRBashNumPypandasscikit-learnSciPy
Databases & Platforms
ChEMBLPubChemPDBUniProtAlphaFoldDBOpenTargets
Visualization & Analytics
MatplotlibTableauPower BI
Cloud & HPC
AWSSLURMHPC Environments
Projects
Selected research projects
Computational pipelines, ML models, and analytical workflows shipped across multiple research collaborations.
RNA-seq Analysis with Deep Learning
End-to-end RNA-seq workflow with deep models for expression-based phenotype prediction.
PyTorchSTARSalmon
Outcome · Improved classification AUC across benchmark datasets.
Molecular Docking & MD Pipeline
Automated docking + MD framework for screening phytochemicals against disease targets.
AutoDockGROMACSBash
Outcome · Pipeline reused across 3 disease projects.
Computational Drug Discovery
Virtual screening of natural compounds for human disease targets.
ChEMBLRDKitML
Outcome · Shortlisted lead candidates for in-vitro studies.
Biomarker Prediction with ML
ML-driven discovery of predictive biomarkers from multi-omics datasets.
scikit-learnXGBoost
Outcome · Robust biomarker panels with cross-cohort validation.
Structural Bioinformatics Workflow
Homology modelling, validation and structure-guided analysis pipeline.
AlphaFoldPyMOLPDB
Outcome · High-confidence models for novel targets.
WGCNA Co-expression Networks
Module discovery and hub-gene identification across stress conditions.
RWGCNA
Outcome · Mapped key regulators of plant stress response.
Plant Stress Biology Analysis
Computational dissection of plant transcriptional response to abiotic stress.
RNA-seqPathway
Outcome · Cataloged stress-responsive regulons.
Microbial Culture & Bioprocess
Data analytics for microbial cell culture optimization and bioprocess scale-up.
PythonStats
Outcome · Optimized productivity in pilot runs.
Publications
Selected publications
2024·Plant Molecular Biology·DOI: 10.xxxx/xxxxx
Functional and structural characterization of bZIP transcription factors
2023·Journal of Molecular Modeling·DOI: 10.xxxx/xxxxx
Computational investigation of compounds for acetylcholinesterase inhibition
2023·Industrial Crops and Products·DOI: 10.xxxx/xxxxx
Aquilaria malaccensis micropropagation and machine learning analysis
2022·Computational Biology and Chemistry·DOI: 10.xxxx/xxxxx
Molecular docking and molecular dynamics studies on disease targets
2022·Frontiers in Plant Science·DOI: 10.xxxx/xxxxx
Genome-wide analysis and computational modeling of stress-response genes
Certifications & Achievements
Recognition & credentials
Certifications
Molecular Dynamics Certification
Udemy
Chem Bio IT-QSAR Workshop 2022
Workshop
NGS Analysis & Drug Discovery Training
IBAB Bangalore
Poster — Intl. Conf. on CRISPR & Computational Biology
Conference
Achievements
01Multiple SCI-indexed research publications
02Cross-disciplinary computational biology expertise
03Multi-omics and structural biology research experience
04AI-assisted biological data analysis
05Collaborative interdisciplinary research
06Strong computational and analytical problem-solving skills
Contact
Let's collaborate
Open to research collaborations, consulting, and industry opportunities in computational biology, multi-omics and AI-assisted healthcare analytics.