arXiv cs.AI· Domenico Amato, Salvatore Calderaro, Giosu\`e Lo Bosco, Riccardo Rizzo, Filippo Vella·· 1 天前AI 评分24
GraphTFactor：基于图的蛋白质转录因子预测框架
GraphTFactor: A graph-based approach for proteins transcription factor prediction
AI 导读
GraphTFactor 是一个将蛋白质序列建模为图结构、直接分类转录因子的端到端框架，可自然处理变长输入且不施加长度约束。实验显示其在基准数据集上优于现有方法，并能给出对预测有贡献的序列区域的可解释性洞察。团队同时开源了软件包和交互式网页应用，支持用户对自定义序列进行转录因子预测。
正文 · 原文
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.
| Subjects: | Quantitative Methods (q-bio.QM); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.40867 [q-bio.QM] |
| (or arXiv:2610.40867v1 [q-bio.QM] for this version) | |
| https://doi.org/10.48550/arXiv.2610.40867 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Salvatore Calderaro Dr [view email]
[v1]
Thu, 29 Oct 2026 18:45:22 UTC (2,939 KB)
来源：arXiv cs.AI · arxiv.org