# Zeyu YANG

- **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:** [ZEYU YANG](https://cerebralvalley.ai/u/TYR00)
- **GitHub:** https://zeyuyang-0420.github.io/PLoTM/ https://github.com/Zeyuyang-0420/PLoTM
- **Demo video:** https://www.youtube.com/watch?v=TjZziQq_UfU
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/106

Disease predisposition arises from complex, multivariate gene regulation, yet conventional approaches typically nominate a single target gene and fail to capture the underlying regulatory network. Two complementary genome-scale resources now make the full network tractable: genome-wide CRISPRi Perturb-seq, which reveals how knocking down any individual gene reshapes the entire transcriptional program landscape, and human loss-of-function (LoF) genetics, which quantifies how a gene's inactivation affects a disease trait. Inspired by a recent Nature framework (Ota et al.), we integrate these to reconstruct, for a chosen disease, a directed network linking regulator genes to gene-expression programs and ultimately to the trait. We demonstrate it on rheumatoid arthritis (RA): non-negative projection of CD4⁺ T-cell Perturb-seq onto RA patient–derived programs, followed by a program-to-trait regression against RA LoF burden, yields the complete regulator→program→trait map. The network recovers the Th17 program—a hallmark pathogenic CD4⁺ T-cell state in RA—as a positive control, and additionally surfaces the SOCS2/CISH cytokine-signalling-feedback program (P19) and its candidate upstream regulators as a novel, testable lead. Finally, we release a reproducible, disease-swappable pipeline, letting the same framework be applied to any trait with matched Perturb-seq and LoF data.

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