A modular framework that turns public RNA-sequencing data into usable findings, published in The Journal of Molecular Diagnostics and tested on three very different disease questions.
The program reads multiple layers of biology from one sample, including circular RNA, and studies patterns across large patient populations Decode will share new research at the ACTRIMS-ECTRIMS and AMP meetings and meet [read more...]
Framework for extracting validated findings from public RNA-sequencing data at any processing stage, applied across three disease contexts to build biomarker panels, prioritize targets, and locate where signals actually live.
Decode contributed transcriptomic analysis and a portion of the expression cohort to this collaborative study linking serum PFHxS and PFOA exposure to multiple sclerosis risk in females. With the University of Miami Miller School of Medicine.
First tool to extract immune gene family signatures - including HLA and KIR activity, diversity, and binding - from standard sequencing data, validated in MS and neuromyelitis optica cohorts across eight institutions.
2026 ACTRIMS Poster Presentation
Large language models from OpenAI and Anthropic are accelerating how teams summarize literature, draft protocols, and brainstorm product ideas. They are truly helpful for many aspects of healthcare work. However, they are not, on their [read more...]
First publication applying Decode's platform to GI/Crohn's disease, extending the validated methodology from MS into a new therapeutic area using administrative claims data.
As GenAI captures a growing share of attention in healthcare, much of the conversation centers on models that create content rather than tools that quietly improve decisions at the point of care. Our new [read more...]
Demonstrated that ML models predicted 90% of total four-month spend for top-cost MS patients vs. 42% with conventional methods. Open-access publication.
Precision medicine R&D platform
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