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。
2023年2月2日,实验室团队在《生物信息学简报》 (Briefings in Bioinformatics) 杂志上在线发表了题为《解读人脑结构和功能的遗传结构:神经影像基因组学最新进展综述》 (“”Deciphering the genetic architecture of human brain structure and function: a brief survey on recent advances of neuroimaging genomics ") 的研究成果。
2023年1月26日,实验室与合作团队在Nature Communications杂志上发表了两篇题为TadA orthologs enable both cytosine and adenine editing of base editors及TadA reprogramming to generate potent miniature base editors with high precision的论文。
2022年11月14日,实验室团队在Genome Biology期刊发表题为metaMIC: Reference-free Misassembly Identification and Correction of de novo metagenomic assemblies的文章。研究团队提出了一种无参的宏基因组组装错误识别以及校正工具metaMIC,能够精确定位组装重叠群(contig)上可能的错误区域。
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