Lung Cancer Biomarker Atlas

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Adhaar Kohli

Master of Technology (M.Tech) — Biotechnology

Transcriptome Laboratory, Center of Excellence in Emerging Diseases
Department of Biotechnology, Jaypee Institute of Information Technology (JIIT)

adhaarkohli@gmail.com LinkedIn

Adhaar Kohli is a Master of Technology graduate in Biotechnology from Jaypee Institute of Information Technology, Noida. His research focuses on integrating multi-omic data with machine learning and bioinformatics to discover clinically actionable biomarkers in lung cancer, with a translational focus on point-of-care biosensor design.

Featured Publication

Review Article • 2026

Decoding Molecular Signatures for miRNA-Based Biosensor Platforms from Nanoscale Engineering to Lung Cancer Diagnostics

Authors: Adhaar Kohli, Ritika Dhigan, Sushma Yadav, Vibha Rani

Analytical Chemistry, 2026, 98(24), 17501-17516

Comprehensive review of miRNA-based biosensor platforms, covering nanoscale engineering approaches for detecting circulating microRNAs in lung cancer diagnostics. The review synthesizes biosensor types, nanomaterials, recognition elements, transduction mechanisms, target miRNAs, sample types, and performance metrics for lung and related biomedical diagnostic platforms.

BiomarkersBiotechnologyCancerGeneticsSensors
DOI: 10.1021/acs.analchem.5c07369

Education

M.Tech in Biotechnology

Jaypee Institute of Information Technology, Noida

2024 – 2026

Thesis: "An Integrated Machine Learning and Bioinformatics Pipeline for Lung Cancer Biomarker Discovery and Biosensor Translation"

B.Tech in Biotechnology

Jaypee Institute of Information Technology, Noida

2020 – 2024

Research Interests

Multi-omic data integrationMachine learning in cancer prognosisInterpretable AI (SHAP, LIME)microRNA-based biosensorsSilicon nanowire FET biosensorsPrecision oncologyTCGA & GEO cohort analysis

Technical Expertise

Python (scikit-learn, PyTorch, TensorFlow)
R (Bioconductor, edgeR, DESeq2)
Bioinformatics pipelines (Snakemake, Docker)
Multi-omic feature engineering
Ensemble ML (Stacking, LightGBM, XGBoost)
Survival analysis (Cox-PH, Kaplan-Meier)
Molecular docking (AutoDock Vina, GROMACS)
Biosensor design (Si-NWFET)

Read the Full Thesis

LungVar-ML — An integrated ML and bioinformatics pipeline for lung cancer biomarker discovery and biosensor translation.

View Dissertation