STAB
Spatio-Temporal cell Atlas of the human Brain
实验室简介
Biomed AI Lab focuses on the cross research of artificial intelligence and biomedicine. The team members come from different disciplines such as computer, mathematics, biology and physics. Based on multimodal biomedical big data, the laboratory develops and applies artificial intelligence algorithm theory and technology for health risk prediction, intelligent diagnosis, treatment and intervention, prognosis evaluation, etc. In recent years, focusing on the characteristics of biomedical big data, a series of artificial intelligence algorithms have been developed, which have been successfully applied to brain-gut axis, brain development, brain diseases and other scenes. Relevant work has been published in Nature, Science, Cell, Cell Metabolism, IEEE TPAMI, Molecular Psychology, Nature Communications and other journals. And has won the first prize of Wu Wenjun Artificial Intelligence Natural Science Award and the second prize of Natural Science of the Ministry of Education. The group has undertaken National Key Research and Development Plan Projects, key and general projects of National Natural Science Foundation of China, and sub projects of Major Science and Technology Projects in Shanghai,etc.
Biomedical artificial intelligence laboratory is a united and progressive scientific research team. Interested candidates can send their resumes to Professor Xing-Ming Zhao. Look forward to your joining!
研究方向
Based on the genomics, transcriptomics and metabolomics data produced by the lab and the public data platforms as well as the long- and short-read sequencing data, we aim to develop algorithms to identify the genetic risk genes and variants of brain diseases in Chinese population, explore the new pathogenesis of brain diseases based on multi-omics data, develop algorithm for molecular diagnosis, and study new diagnosis and treatment methods using big data.
Based on brain fMRI data, genomics data, electronic medical records, behavior data, environmental factors and other data types produced by the lab and global public databases, we will develop integrative methods based on imaging, molecule and behavior data using machine learning and deep learning models, identify the risk factors of brain diseases, and aim to provide intelligent diagnosis for brain diseases.
Based on the microbiome data, metabolomics data and MRI data produced by the lab and public data platforms, we carry out studies on the data analysis and algorithm development of metagenomics data by integrating long- and short-read sequencing data, microbial species identification and gene function analysis, virus-bacteria interaction prediction, association analysis between microbiome data and imaging, behavior and genomics data.
2023年8月11日,实验室团队在Microbiome上发表了题为Enterotypes of the human gut mycobiome的研究论文。
2023年7月24日, 实验室团队在Genome Medicine上发表题为Prioritizing genes associated with brain disorders by leveraging enhancer-promoter interactions in diverse neural cells and tissues的研究论文。
2023年5月29日,实验室团队在IEEE Journal of Biomedical and Health Informatics上发表了题为《Improving Alzheimer’s Disease Diagnosis with Multi-Modal PET Embedding Features by a 3D Multi-task MLP-Mixer Neural Network》的文章,提出了一种新的方法,提高了阿尔茨海默病辅助诊断的准确性。
2023年5月4日,实验室团队在Gut Microbes期刊发表题为Performance of Gut Microbiome as an Independent Diagnostic Tool for 20 Diseases: Cross-Cohort Validation of Machine-Learning Classifiers的文章。
2023年4月27日, 实验室团队在《生物信息学简讯》(Briefings in Bioinformatics)杂志在线发表了题为《基因组解析肠道宏基因组学的计算策略研究》(“A survey on computational strategies for genome-resolved gut metagenomics”)的文章。
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