Deep Learning-based Biomarker Discovery for Disease Diagnosis

Abstract

This research paper explores the application of deep learning models in biomarker discovery for disease diagnosis. The use of biomarkers, which are measurable indicators of biological processes, is crucial for early detection and accurate diagnosis of diseases. Traditional methods for biomarker discovery often rely on statistical analysis and experimentation, which can be time-consuming and expensive. Deep learning offers a promising alternative by leveraging large datasets to identify patterns and relationships in complex biological data. This paper discusses the advantages and challenges of using deep learning for biomarker discovery and provides a comprehensive review of recent studies in this field. Furthermore, it presents a case study demonstrating the application of deep learning in discovering biomarkers for a specific disease. Overall, this paper highlights the potential of deep learning to revolutionize biomarker discovery and improve disease diagnosis outcomes.