Application of Deep Learning for Early Disease Diagnosis and Biomarker Discovery

Abstract

Major diseases such as cancer, neurodegenerative diseases and cardiovascular diseases have had serious impacts on the global public health system. Early diagnosis is key for improving treatment outcomes, reducing mortality rates and alleviating the socioeconomic burden. With the rapid development of technologies such as high-throughput sequencing, single-cell omics and spatial transcriptomics, biomedical research has entered a new data-driven stage. How to effectively mine the key information related to the early stage of disease from these complex and high-dimensional multiomics data has become the core issue of current research. This article systematically reviews the research progress of deep learning in the early diagnosis of diseases and the discovery of biomarkers. First, the basic principles of deep learning and its advantages in processing biomedical data were introduced. Subsequently, its typical applications in transcriptomics, proteomics, single-cell and spatial omics, as well as multiomics integrated analysis, were expounded. Meanwhile, the potential value of deep learning in noninvasive detection, such as liquid biopsy, was discussed. The results show that deep learning can automatically extract key features from complex biological data and identify early disease signals that are difficult to detect via traditional methods, providing a new technical approach for disease prediction and precise diagnosis. However, issues such as data heterogeneity, insufficient interpretability of the model, and obstacles to clinical translation remain the main factors restricting its wide application. Future research should focus on enhancing model transparency, sharing high-quality data, and establishing interdisciplinary collaboration mechanisms to accelerate the clinical application and promotion of deep learning in the field of precision medicine.