Deep Learning in Gene Regulatory Network Inference: A Survey.

Abstract

Understanding the intricate regulatory relationships among genes is crucial for comprehending the development, differentiation, and cellular response in living systems. Consequently, inferring gene regulatory networks (GRNs) based on observed data has gained significant attention as a fundamental goal in biological applications. The proliferation and diversification of available data present both opportunities and challenges in accurately inferring GRNs. Deep learning, a highly successful technique in various domains, holds promise in aiding GRN inference. Several GRN inference methods employing deep learning models have been proposed; however, the selection of an appropriate method remains a challenge for life scientists. In this survey, we provide a comprehensive analysis of 12 GRN inference methods that leverage deep learning models. We trace the evolution of these major methods and categorize them based on the types of applicable data. We delve into the core concepts and specific steps of each method, offering a detailed evaluation of their effectiveness and scalability across different scenarios. These insights enable us to make informed recommendations. Moreover, we explore the challenges faced by GRN inference methods utilizing deep learning and discuss future directions, providing valuable suggestions for the advancement of data scientists in this field.