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GraphTFactor applies graph-based deep learning to classify transcription factors directly from protein sequences, handling variable-length inputs without truncation or padding. The paper reports gains over existing methods on benchmarks and offers per-residue interpretability, but those claims rest on the authors' own evaluation and need independent replication. For ML readers, the main interest is the sequence-to-graph encoding as a representation choice for biological data.
GraphTFactor: A graph-based approach for proteins transcription factor prediction
arXiv:2610.40867v1 Announce Type: cross Abstract: Transcription factors (TFs) play an important role in regulating gene expression, making their identification a fundamental task in computational biology. In this work, we propose GraphTFactor, a novel end-to-end framework that models protein sequences as graphs and directly classifies transcription factors. By encoding sequences into graph structures, the proposed approach naturally handles variable-length inputs while preserving relevant relational information, without imposing length constraints. Experimental results show that GraphTFactor outperforms state-of-the-art methods on benchmark datasets, demonstrating the effectiveness of graph-based representations for TF prediction while providing interpretable insights into the sequence regions contributing to the model's predictions. To support reproducibility and practical adoption, we release an open-source software package and an interactive web application that let users easily perform TF prediction on custom sequences.
Abstract:Transcription factors (TFs) play an important role in regulating gene expression, making their identification a fundamental task in computational biology. In this work, we propose GraphTFactor, a novel end-to-end framework that models protein sequences as graphs and directly classifies transcription factors. By encoding sequences into graph structures, the proposed approach naturally handles variable-length inputs while preserving relevant relational information, without imposing length constraints. Experimental results show that GraphTFactor outperforms state-of-the-art methods on benchmark datasets, demonstrating the effectiveness of graph-based representations for TF prediction while providing interpretable insights into the sequence regions contributing to the model's predictions. To support reproducibility and practical adoption, we release an open-source software package and an interactive web application that let users easily perform TF prediction on custom sequences.
Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
arXiv:2610.40867 [q-bio.QM]
(or arXiv:2610.40867v1 [q-bio.QM] for this version)
https://doi.org/10.48550/arXiv.2610.40867 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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