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arXiv Machine Learning By Arezoo Bybordi, Favour Achimba, Mariam Gelashvili, Jessy Ramirez, Weigang Qiu, Mand\"e Holford, Anita Raja

Escaping the Phylogenetic Trap: Persistent Homology Enables Cross-Species Ion Channel Classification via Multimodal Graph Neural Networks

Read the original on arXiv Machine Learning →

The paper investigates how phylogenetic leakage can inflate performance estimates for protein classifiers, particularly in venom ion‑channel target prediction. By treating cross‑species prediction as a structured distribution shift, the authors compare five structural inductive biases using a multimodal graph neural network that fuses ESM‑2 embeddings with a disulfide‑aware attention mechanism. Persistent‑homology representations, which capture backbone geometry through topological descriptors, outperform other biases under a leave‑one‑taxon‑out protocol, achieving 63.4% accuracy versus 54‑55% for other geometric priors and 30% for a sequence‑only baseline.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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