Peer-reviewed.
Clinically validated.
Co-clinical validation of agentic targeting frameworks
Rigorous multi-cohort evaluation confirming the accuracy and clinical utility of autonomous variant interpretation engines across complex oncology cohorts.
Targeted therapeutics vs. autonomous genomic modeling
A comprehensive comparative analysis benchmarking predictive precision care pipelines against conventional treatment decision trees.
Digital twins in oncology: Real-world therapeutic pipelines
Demonstrating actionable therapeutic insight improvements via live pipeline simulations mapping real-time patient progression markers.
Machine learning optimizations in variant filtering
Iterative neural model applications achieving unprecedented false-positive reductions across high-throughput raw genomics sequence inputs.
GeneSilico
in the news.
Prof. Debarka Sengupta Honored With Merck Young Scientist Award, 2023
The award ceremony, held in Bangalore on November 24th, 2023, stands as a significant milestone in Prof. Sengupta’s illustrious scientific journey, highlighting his unwavering dedication and outstanding contributions to advancing AI applications in cancer research.
Recognized by
leading institutions.
Nature Publication
Peer-reviewed research published in Nature — the world's leading scientific journal detailing clinical framework utility.
Humboldt Grant
Alexander von Humboldt Stiftung research award for breakthrough contributions to autonomous oncology AI modeling.
ICMR Validation
Selected for India's national clinical validation study across healthcare infrastructure by the Indian Council of Medical Research.
10 Patents Filed
Ten core utility patents covering the causal AI computation engines, Digital Twin construction models, and sequence workflows.
Govt. Recognition
Formally recognized for high-impact contributions engineered to scale national healthcare artificial intelligence infrastructure.
Epic Certification
Certified on Epic Marketplace for native EHR data structure integration across high-throughput clinical networks.
Thinking about
precision oncology.
Why causal AI beats correlation for cancer therapy selection
Most oncology AI predicts from population patterns. We explain why that fundamentally misses the point — and what causal reasoning changes.
Read ArticleHow early resistance detection changes the economics of cancer care
Catching resistance 7 weeks earlier doesn't just save response time — it changes the cost structure of an entire treatment course.
Read ArticleWhat patients actually want from a cancer AI companion
We spoke to 40 patients across India about what they needed most during treatment. The answers surprised us — and shaped MyCasebook.
Read Article
See the science
in the clinic.
Request a demo or explore what each product does.