Biological Data Resources and Machine Learning Frameworks for Hematology Research.

Abstract

Hematology research has greatly benefited from the integration of diverse biological data resources and advanced machine learning 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 machine learning algorithms to analyze large-scale biological data, researchers are able to 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 machine learning frameworks pertinent to hematology research.