arXiv Machine Learning November 2, 2026 By Domenico Amato, Salvatore Calderaro, Giosu\`e Lo Bosco, Riccardo Rizzo, Filippo Vella

GraphTFactor: A graph-based approach for proteins transcription factor prediction

Read the original on arXiv Machine Learning →

GraphTFactor is a new end-to-end framework that represents protein sequences as graphs to directly classify transcription factors. By encoding sequences into graph structures, it handles variable-length inputs and preserves relational information without length constraints. Experiments show that GraphTFactor outperforms state‑of‑the‑art methods on benchmark datasets and offers interpretable insights into sequence regions driving predictions, with an open-source package and web app for easy use.

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