# Kinga Zielinska

- **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:** [Kinga Zielinska](https://cerebralvalley.ai/u/Kinzie)
- **GitHub:** https://github.com/Kizielins/Anthropic_hackathon_Claude_Science
- **Demo video:** https://youtu.be/rWXCYf2jW0w
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/15

We set out to build a machine-learning classifier that predicts a clinically meaningful vaginal-microbiome state from a baseline metagenomic profile, using only public data. Because intervention-response labels proved unrecoverable from public deposits — probiotic/live-biotherapeutic response had zero read-joinable labels, and preterm-birth cohorts are dominated by V3–V4 16S that resolves L. iners from L. crispatus poorly — we selected the one abundant, per-sample, self-supervised target the data could support: next-day community-state-type (CST) transition. The cohort was PRJEB37731 (Danish daily shotgun metagenomics, 40 subjects, ~967 consecutive day-pairs), reached through a documented funnel of 90 public datasets → 30 mined publications → 4 candidate targets → 8 feasibility-vetted cohorts → 1.

The classifier was engineered for rigor: 184 CLR-transformed species plus covariates, leave-one-subject-out cross-validation, honest baselines (prevalence, persistence, current-state), subject-clustered GEE for effect estimates, and a synthetic-data power analysis. The primary result was an honest negative — the full microbiome composition adds essentially nothing beyond the current-state label (transition AUROC 0.66, matched by a covariate baseline), and next-day onset of dysbiosis is unpredictable (AUROC 0.51–0.55; up to 5 days of memory does not help).

We then tested the textbook claim that L. iners is a "gateway to dysbiosis." iners strongly predicts community movement (adjusted OR 5.87, 95% CI 3.15–10.94, p=2.5×10⁻⁸), but that movement is predominantly recovery toward L. crispatus (0.57) rather than descent to dysbiosis (0.24). L. iners marks mobility, not decline — a novel refinement of the field's model.

Why it matters: BV recurs in >50% of women within 6–12 months, making treatment response fundamentally a prediction problem. This is a first-of-its-kind, fully leakage-safe attempt to predict near-term vaginal-microbiome dynamics from public data — it honestly maps what is and isn't predictable at current data scale, and delivers a reproducible pipeline ready for a future cohort with linked intervention outcomes.

---

Markdown version of https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/15. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt