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.
2024年1月19日,复旦大学赵兴明教授实验室与合作团队在Advanced Science上发表题为Efficient Recovery of Complete Gut Viral Genomes by Combined Short- and Long-Read Sequencing的研究论文。
2023年12月13日, 实验室团队在PLOS Genetics上发表了题为An augmented Mendelian randomization approach provides causality of brain imaging features on complex traits in a single biobank-scale dataset的研究论文。
2023年11月22日,实验室与联合团队在胃肠病学顶级期刊Gut在线发表题为“Compared to histamine-2 receptor antagonist, proton pump inhibitor induces stronger oral-to-gut microbial transmission and gut microbiome alterations: a randomized controlled trial”的学术论文。
2023年10月30日,实验室团队在Nucleic Acids Research上发表题为STAB2: an updated spatio-temporal cell atlas of the human and mouse brain(STAB2:人和小鼠大脑的更新时空细胞图谱)的研究论文。
2023年10月20日,实验室与合作团队在Nucleic Acids Research发表题为Engineering of cytosine base editors with DNA damage minimization and editing scope diversification的研究论文。
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