Predictive models of DNA chromatin profile (i.e. epigenetic state), such as transcription factor binding, are essential for understanding regulatory processes and developing gene therapies. It is known that the 3D genome, or spatial structure of DNA, is highly influential in the chromatin profile. Deep neural networks have achieved state of the art performance on chromatin profile prediction by using short windows of DNA sequences independently. These methods, however, ignore the long-range dependencies when predicting the chromatin profiles because modeling the 3D genome is challenging.

中文翻译：

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图卷积网络用于使用序列和3D基因组数据预测表观遗传状态

DNA染色质分布（即表观遗传状态）的预测模型，例如转录因子结合，对于理解调节过程和开发基因疗法至关重要。已知3D基因组或DNA的空间结构对染色质分布有很大影响。深度神经网络通过独立使用短序列的DNA序列，在染色质分布预测方面取得了最新技术。但是，由于建模3D基因组具有挑战性，因此在预测染色质图谱时，这些方法会忽略远程依赖性。