用机器学习解析基因调控网络，助力生物机制研究
Machine Learning Methods for Gene Regulatory Network Inference
- 融合监督、无监督等多类机器学习方法分析组学数据
- 深度学习显著提升基因调控关系推断准确率
- 适合生物信息学与人工智能交叉研究者参考
基因调控网络（GRNs）是响应环境与发育信号调控基因表达的复杂生物系统。随着计算生物学和高通量测序技术的发展，基因调控网络的推断与建模精度大幅提升。现代方法越来越多地采用人工智能技术，特别是监督、无监督、半监督及对比学习等机器学习方法，分析大规模组学数据，揭示基因间的调控关系。为支持基因调控研究及新型机器学习方法的开发，本文全面综述了基于机器学习的基因调控网络推断方法，涵盖常用数据集与评估指标，并重点探讨前沿深度学习技术在提升推断性能中的作用。同时，也展望了未来改进基因调控网络推断的潜在方向。
原文摘要 · Abstract (English)
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.
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