# Sriram

- **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:** [sriram vijendran](https://cerebralvalley.ai/u/Ramviji)
- **GitHub:** https://sriram98v.github.io/claude-science-hackathon/
- **Demo video:** https://youtu.be/HAUcofbm9U8
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/169

This project presents a causal-structure-oriented, interpretable pipeline for modeling influenza (H3N2) antigenic distance from hemagglutination-inhibition (HI) data and HA sequence alignments. Its central problem is that dense viral phylogenies tightly link true antibody-escape drivers with passenger mutations, so accurate sequence-based prediction does not establish which HA positions physically disrupt antibody binding. In this study, we analyzed two public H3N2 HI datasets by collapsing co-evolving positions into linkage blocks, then applying target-oriented causal discovery ranked by 200-resample bootstrap stability, alongside an interpretable B-spline Kolmogorov–Arnold Network whose per-position response curves are inspectable and which extends to second order to probe epistasis. The first-order KAN trails gradient boosting by a small but robust margin; a second-order KAN closes most of the gap only under a different protocol. Our pipeline demonstrates cross-method convergence by isolating a highly stable set of key drivers at mature HA positions 133, 156/158, and 189, mapping precisely to classical antigenic sites A and B. Finally, adjusted partial-regression effect sizes systematically shrink relative to marginal associations, confirming successful control over phylogenetic confounding.

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Markdown version of https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/169. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt