Yi 易莹 Ying, Hu 胡永飞 Yongfei, Kang 康娟娟 Juanjuan, Liu 刘启发 Qifa, Huang 黄燕 Yan, Wang 王栋 Dong
Institute of Dermatology and Venereology, Dermatology Hospital, Southern Medical University, Guangzhou 510091, China.
Department of Bioinformatics, Guangdong Province Key Laboratory of Molecular Tumor Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.
Genomics Proteomics Bioinformatics. 2025 May 30;23(2). doi: 10.1093/gpbjnl/qzaf021.
Hematology research has greatly benefited from the integration of diverse biological data resources and advanced machine learning (ML) frameworks. This integration has not only deepened our understanding of blood diseases such as leukemia and lymphoma, but also enhanced diagnostic accuracy and personalized treatment strategies. By applying ML algorithms to analyze large-scale biological data, researchers can more effectively identify disease patterns, predict treatment responses, and provide new perspectives for the diagnosis and treatment of hematologic disorders. Here, we provide an overview of the current landscape of biological data resources and the application of ML frameworks pertinent to hematology research.
血液学研究从多种生物数据资源与先进机器学习（ML）框架的整合中受益匪浅。这种整合不仅加深了我们对白血病和淋巴瘤等血液疾病的理解，还提高了诊断准确性并完善了个性化治疗策略。通过应用ML算法来分析大规模生物数据，研究人员能够更有效地识别疾病模式、预测治疗反应，并为血液系统疾病的诊断和治疗提供新的视角。在此，我们概述了生物数据资源的当前状况以及与血液学研究相关的ML框架的应用。
