IEEE J Biomed Health Inform. 2023 Sep;27(9):4559-4568. doi: 10.1109/JBHI.2023.3292299. Epub 2023 Sep 6.
Identification of chromatin interactions is crucial for advancing our knowledge of gene regulation. However, due to the limitations of high-throughput experimental techniques, there is an urgent need to develop computational methods for predicting chromatin interactions. In this study, we propose a novel attention-based deep learning model, termed IChrom-Deep, to identify chromatin interactions using sequence features and genomic features. The experimental results based on the datasets of three cell lines demonstrate that the IChrom-Deep achieves satisfactory performance and is superior to the previous methods. We also investigate the effect of DNA sequence and associated features and genomic features on chromatin interactions, and highlight the applicable scenarios of some features, such as sequence conservation and distance. Moreover, we identify a few genomic features that are extremely important across different cell lines, and IChrom-Deep achieves comparable performance with only these significant genomic features versus using all genomic features. It is believed that IChrom-Deep can serve as a useful tool for future studies that seek to identify chromatin interactions.
鉴定染色质相互作用对于深入了解基因调控至关重要。然而，由于高通量实验技术的限制，迫切需要开发用于预测染色质相互作用的计算方法。在这项研究中，我们提出了一种新颖的基于注意力的深度学习模型，称为 IChrom-Deep，该模型使用序列特征和基因组特征来识别染色质相互作用。基于三个细胞系数据集的实验结果表明，ICharm-Deep 表现出令人满意的性能，优于以前的方法。我们还研究了 DNA 序列和相关特征以及基因组特征对染色质相互作用的影响，并强调了一些特征（如序列保守性和距离）的适用场景。此外，我们确定了一些在不同细胞系中极其重要的基因组特征，并且 IChrom-Deep 仅使用这些重要的基因组特征就能实现与使用所有基因组特征相当的性能。相信 IChrom-Deep 可以成为未来研究识别染色质相互作用的有用工具。
