Deep Learning and Machine Learning Applications in Biomedicine.

This document discusses the applications of Artificial Intelligence (AI), specifically deep learning and machine learning, in the field of biomedicine. It focuses on the use of AI in genomics, transcriptomics, and proteomics, highlighting its potential in disease diagnosis, precision medicine, drug discovery, and understanding pathogenic mechanisms. The paper provides examples of successful AI applications in these areas, such as deep learning tools for analyzing DNA sequence data, predicting gene functions, and studying epigenetic factors. It also discusses the use of AI in transcriptomic data analysis, including single-cell and spatial resolution studies, as well as its role in proteomics, such as predicting protein structures and functions. The document acknowledges the challenges faced by AI in the life sciences, such as dataset type and size, and emphasizes the need for further research to improve algorithm efficiency and model interpretability. Overall, the paper highlights the potential of AI in omics data analysis and its contribution to advancing bioinformatics and life science research. [Extracted from the article]