

Senior Scientist with 9+ years of expertise in developing systems biology models and building gene regulatory networks. Proven ability to integrate diverse data sources to uncover predictive novel biomarkers and enhance therapeutic efficacy.
Computational Biology & Bioinformatics
Multi-omics Data Integration
Gene Set Enrichment Analysis (GSEA)
Weighted Gene Co-expression Network Analysis (WGCNA)
Machine Learning & Statistical Analysis
Supervised and Unsupervised learning
Spearman Correlation
Mann–Whitney U Test
Fisher's Exact Test
Multiple Testing Correction (Benjamini–Hochberg FDR)
Scientific Literature Interpretation
Cancer genomic analysis
Clinical trial analysis
Programming & Data Analysis
Python
SQL
Professional Skills
Scientific Writing
Technical Presentations
Training & Mentoring
www.linkedin.com/in/nagendra-prasad-k-b07561ab
https://github.com/nagendraprasad94-ops/Gene-Set-Enrichment-Analysis/tree/main
Publication at American society of clinical oncology: Cellworks Omics Biology Modeling (CBM) to predict therapy response and identifies novel biomarkers for carboplatin/cisplatin along with pemetrexed in NSCLC patients.
Python
Therapeutic Expertise
Microtubule-Targeting Agents : Paclitaxel, Docetaxel, Vincristine, Vinblastine, Eribulin (microtubule dynamics, mitotic arrest, resistance mechanisms).
DNA Damaging agents : 5-Fluorouracil (5-FU), Capecitabine, Trifluridine, Temozolomide, Procarbazine, Dacarbazine, Olaparib, Niraparib (HR, MMR, BER, NER, PARP trapping, synthetic lethality).
Targeted Small Molecule Inhibitors : EGFR inhibitors (Gefitinib, Erlotinib, Afatinib, Osimertinib); MEK inhibitors (Trametinib, Cobimetinib, Binimetinib, Selumetinib); AKT inhibitors (Capivasertib).
Targeted Antibody Therapeutics : Monoclonal antibodies (Pertuzumab); Antibody–Drug Conjugates (Brentuximab Vedotin);
Immune Checkpoint Inhibitors: Anti-PD-1 : Nivolumab, Pembrolizumab. Anti-PD-L1 : Atezolizumab, Durvalumab, Avelumab, Anti-CTLA-4: Ipilimumab, Tremelimumab
Machine learning
Python