# AstroOmix

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Yoga Mithra Senthil Kumar](https://cerebralvalley.ai/u/Mithrasen)
- **GitHub:** https://github.com/Mithrasen/AstroOmix , https://astroomix.streamlit.app
- **Demo video:** https://youtu.be/1MfycsEtySQ
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/152

AstroOmix is a Claude-powered research application for analysing space-biology data without overstating what limited datasets can support.

Researchers can upload their own data or explore real NASA studies through two workflows: differential-expression analysis for replicated spaceflight RNA-seq experiments, and longitudinal modelling of physiological measurements across pre-flight, flight and recovery phases. Deterministic tools perform the statistics, while an embedded Claude agent explains results, answers study-specific questions and retrieves relevant PubMed evidence.

AstroOmix is designed around verifiability. Numerical claims are checked at runtime against structured analysis outputs; unsupported figures are withheld. Literature citations must originate from retrieved records, and literature consistency is presented as context, not proof. The application also flags thin time series, distinguishes interpolation from extrapolation and avoids clinical conclusions.

Building AstroOmix highlighted a central challenge in space-health research: the available data are valuable, but small, heterogeneous and easy to overinterpret. The project therefore focuses not on automating certainty, but on making analysis traceable, reproducible and appropriately cautious.

As longer missions generate increasingly complex biological data, tools that combine computational analysis with evidence-aware AI reasoning can help researchers reach useful conclusions while remaining explicit about uncertainty.

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