Brief Bioinform. 2024 Jul 25;25(5). doi: 10.1093/bib/bbae364.
The development of omics technologies has driven a profound expansion in the scale of biological data and the increased complexity in internal dimensions, prompting the utilization of machine learning (ML) as a powerful toolkit for extracting knowledge and understanding underlying biological patterns. Kidney disease represents one of the major growing global health threats with intricate pathogenic mechanisms and a lack of precise molecular pathology-based therapeutic modalities. Accordingly, there is a need for advanced high-throughput approaches to capture implicit molecular features and complement current experiments and statistics. This review aims to delineate strategies for integrating multi-omics data with appropriate ML methods, highlighting key clinical translational scenarios, including predicting disease progression risks to improve medical decision-making, comprehensively understanding disease molecular mechanisms, and practical applications of image recognition in renal digital pathology. Examining the benefits and challenges of current integration efforts is expected to shed light on the complexity of kidney disease and advance clinical practice.
组学技术的发展推动了生物数据规模的深刻扩展和内部维度的复杂性增加，促使机器学习 (ML) 作为提取知识和理解潜在生物模式的强大工具包得到广泛应用。肾脏疾病是全球主要的健康威胁之一，其发病机制复杂，缺乏精确的基于分子病理学的治疗方法。因此，需要采用先进的高通量方法来捕捉隐含的分子特征，并补充当前的实验和统计方法。本综述旨在描述将多组学数据与适当的 ML 方法相结合的策略，重点介绍关键的临床转化场景，包括预测疾病进展风险以改善医疗决策、全面了解疾病分子机制以及在肾脏数字病理学中进行图像识别的实际应用。研究当前整合工作的优缺点有望揭示肾脏疾病的复杂性并推动临床实践。
