In a paper published in National Science Review, the team of Pro. Liu present an innovative computational framework, the sample-perturbed Gaussian graphical model (sPGGM), designed to analyse disease progression and identify pre-disease stages at the specific sample/cell level based on optimal transport theory and Gaussian graphical models. The proposed sPGGM provides a new single-sample way to identify the pre-disease state and discover signaling molecules leading to potential disease, which showcases exceptional effectiveness and robustness for both bulk and single-cell data analyses, offering a novel perspective for personalized disease prediction.

Recently, Professor Jiao Licheng's team at Xidian University conducted a systematic and in-depth review of the integration of large language models and evolutionary algorithms. The review, titled "When Large Language Models Meet Evolutionary Algorithms: Potential Enhancements and Challenges," was published in Research.