Applications of deep learning in understanding gene regulation

Gene regulation is a central topic in cell biology. Advances in omics technologies and the accumulation of omics data have provided better opportunities for gene regulation studies than ever before. For this reason deep learning, as a data-driven predictive modeling approach, has been successfully applied to this field during the past decade. In this article, we aim to give a brief yet comprehensive overview of representative deep-learning methods for gene regulation. Specifically, we discuss and compare the design principles and datasets used by each method, creating a reference for researchers who wish to replicate or improve existing methods. We also discuss the common problems of existing approaches and prospectively introduce the emerging deep-learning paradigms that will potentially alleviate them. We hope that this article will provide a rich and up-to-date resource and shed light on future research directions in this area.

Li, Z., Gao, E., Zhou, J., Han, W., Xu, X., & Gao, X. (2023). Applications of deep learning in understanding gene regulation. Cell Reports Methods, 3(1), 100384.

This work was supported by Office of Research Administration (ORA) at KAUST under award numbers FCC/1/1976-44-01, FCC/1/1976-45-01, URF/1/4098-01-01, URF/1/4352-01-01, REI/1/5202-01-01, REI/1/4940-01-01, RGC/3/4816-01-01, and REI/1/0018-01-01.

Elsevier BV

Cell reports methods


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