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8月31日,复旦大学类脑智能科学与技术研究院生物医学人工智能团队青年研究员路易斯·佩德罗·科埃略(Luis Pedro Coelho)、特聘教授赵兴明团队,与皮尔·伯克(Peer Bork)团队合作研究,在Nature Communications上发表了题为《A catalog of small proteins from the global microbiome》的研究论文。
2024年6月26日,复旦大学赵兴明教授团队在Science Advances上发表了题为Long-read sequencing reveals extensive gut phagenome structural variations driven by genetic exchange with bacterial hosts的研究论文。
2024年6月21日,复旦大学赵兴明教授团队的研究工作“VirRep: a hybrid language representation learning framework for identifying viruses from human gut metagenomes”被Genome Biology录用。
北京时间2024年6月6日,复旦大学类脑智能科学与技术研究院生物医学人工智能团队赵兴明教授以及青年研究员路易斯·佩德罗·科埃略(Luis Pedro Coelho),与来自美国与德国的科学家合作研究,在Cell主刊上发表了题为Discovery of antimicrobial peptides in the global microbiome with machine learning的研究论文。
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。
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