Topologically associating domains (TADs) are essential units of genome architecture, influencing transcriptional regulation and diseases. Despite numerous methods proposed for TAD identification, it remains challenging due to complex background and nested TAD structures. We introduce HTAD, a human-in-the-loop TAD caller that combines machine learning with human supervision to achieve high accuracy. HTAD begins with feature extraction for potential TAD border pairs, followed by an interactive labeling process through active learning. Performance assessments using public curation and synthetic datasets demonstrate HTAD’s superiority over other state-of-the-art methods and reveal highly hierarchical TAD structures, offering a human-in-the-loop solution for detecting complex genomic patterns.

中文翻译：

---

HTAD：用于监督染色质结构域检测的人机协同框架


拓扑关联结构域 （TAD） 是基因组结构的重要组成部分，影响转录调控和疾病。尽管提出了许多用于 TAD 鉴定的方法，但由于复杂的背景和嵌套的 TAD 结构，它仍然具有挑战性。我们介绍了 HTAD，这是一种人机协同 TAD 调用方，它将机器学习与人工监督相结合，以实现高准确性。HTAD 从潜在 TAD 边界对的特征提取开始，然后通过主动学习进行交互式标记过程。使用公共管理和合成数据集进行的性能评估表明 HTAD 优于其他最先进的方法，并揭示了高度分层的 TAD 结构，为检测复杂的基因组模式提供了人在环解决方案。