With the advancement of biotechnologies, such as single-cell sequencing and spatial multi-omics techniques, a vast amount of data in the field of life sciences requires further interpretation. Our research group primarily employs tools, such as sparse optimization, low-rank approximation, deep learning, and others, to develop appropriate analytical methods. These methods aim to assist biologists in extracting and interpreting the mathematical principles underlying the data and corresponding biological processes. Our research outcomes are poised to accelerate progress in life science research, particularly in areas such as cancer, and contribute significantly to the advancement of precision medicine.