Machine Learning Methods for Gene Regulatory Network Inference
It addresses the problem of understanding gene regulation for biologists and computational researchers, but it is incremental as it synthesizes existing methods rather than introducing new ones.
This paper reviews machine learning methods for inferring gene regulatory networks, highlighting how advances in computational biology and AI have improved accuracy, with a focus on deep learning techniques to enhance performance.
Gene Regulatory Networks (GRNs) are intricate biological systems that control gene expression and regulation in response to environmental and developmental cues. Advances in computational biology, coupled with high throughput sequencing technologies, have significantly improved the accuracy of GRN inference and modeling. Modern approaches increasingly leverage artificial intelligence (AI), particularly machine learning techniques including supervised, unsupervised, semi-supervised, and contrastive learning to analyze large scale omics data and uncover regulatory gene interactions. To support both the application of GRN inference in studying gene regulation and the development of novel machine learning methods, we present a comprehensive review of machine learning based GRN inference methodologies, along with the datasets and evaluation metrics commonly used. Special emphasis is placed on the emerging role of cutting edge deep learning techniques in enhancing inference performance. The potential future directions for improving GRN inference are also discussed.