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The combination of bioinformatics and artificial intelligence (AI) has made significant progress in the current phase. AI technology, especially deep learning, has been widely used in biology, resulting in many innovations. Currently, AI plays a key role in genomics, proteomics, and drug discovery. Deep learning models are used to predict protein structures, discover potential drug compounds, interpret genomic sequences, analyze medical images, and make personalized medical recommendations. In addition, AI can also help accelerate the processing and interpretation of biological big data, helping biologists to understand complex problems in the life sciences more deeply. Compared to traditional genetic data analysis methods, AI combined with bioinformatics methods are often faster and more accurate, and are capable of processing large-scale, high-dimensional biological data, opening up unprecedented opportunities for life science research. In this paper, the whole genome data and biomedical imaging data are used from the perspective of Bayesian hypothesis testing. Genome-wide association analysis, led by large-scale multiple tests, is a very popular tool for identifying genetic variation points in new complex diseases. In genome-wide association analysis, tens of thousands of SNPS need to be tested simultaneously to find out some SNPS related to traits. These tests are related due to factors such as linkage imbalances in the genetic process, and the test questions are set against the background of high-dimensional data (p >=n).
Shen et al. (Thu,) studied this question.