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Festival of Genomics, Biodata & AI 2026

Meet Excelra at festival of genomics 2026

Where Genomics, Biodata, and AI converge — So should your strategy

The life sciences industry is at an inflection point. Multi-omics is generating data at unprecedented scale, AI is reshaping how that data becomes insight, and the pressure to translate discovery into patient impact has never been greater.
Excelra sits at the center of that convergence. We help pharma, biotech, and research organizations integrate complex scientific data, break down silos, and operationalize AI across the R&D value chain — from target discovery to translational research.
Stop by Booth #11 to see how we’re helping leading life sciences organizations move faster, with greater confidence, from data to decisions.

What you’ll discover at booth #11

Bioinformatics & Multi-Omics at scale- Accelerate target discovery, biomarker identification, and translational research with Excelra’s bioinformatics expertise across genomics, transcriptomics, proteomics, and single-cell analytics.
Scientific & Lab informatics- Connect fragmented R&D data ecosystems with our Scientific Data Management, Lab Informatics, and Cloud Enablement services — built for FAIR, AI-ready data.

AI-Ready data & the Excelra AI lab From data curation and FAIRification to LLM-powered scientific applications, see how our AI Lab is putting agentic and generative AI to work in drug discovery.

Curated knowledgebases — GOSTAR™ & beyond- Explore the GOSTAR™ family (Small Molecules, TPD, Large Molecules) and our Custom Biomarker Knowledgebase — trusted by global pharma to power discovery decisions.

Register Now
Date: June 3, 2026
Location: MCEC Boston

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Featured talk on “Towards Personalized Therapeutics: Machine Learning-Driven Integration of Pharmacokinetics and Response Biomarkers”

Speaker

Puneet Saxena

Director
 Computational Biology, Excelra

Adrienne Yanez

Scientific Manager – I
Bioinformatics, Excelra

Track: AI in drug discovery stage

Inter-patient variability in pharmacokinetics (PK) and therapeutic response is a key challenge in precision medicine. Machine learning-based pharmacogenomics can address this by identifying genomic variants associated with differences in drug clearance, AUC, and Cmax, enabling mechanistic insights into patient-specific PK variability and supporting dose optimization.
Beyond single-modality analyses, integrative machine learning workflows applied to multi-modal data- including genomics, blood-based molecular profiling, clinical variables, gene expression, and treatment response- allow simultaneous investigation of PK determinants and therapeutic response biomarkers. This enables patient stratification and prediction of treatment sensitivity or resistance.
To address challenges such as high dimensionality, heterogeneity, and small sample sizes, structured pipelines that incorporate data imputation, feature selection, regularization, and cross-validation are essential. Together, this framework advances the identification of both PK and response biomarkers, supporting the goals of personalized therapeutics

Meet our Experts

Abhishek Nainani

Director
Key Accounts & Business Development

Anukana Bhattacharjee

Business Development Manager
Sales

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