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Welcome to DeepOmics Lab

We are a multidisciplinary research team at the intersection of omics analysis and artificial intelligence, dedicated to developing and utilizing machine learning, deep learning, and bioinformatics algorithms to address the challenges of deciphering the structure and function of biomolecules in life omics.

The research group plans to recruit 1-2 master's and joint doctoral students each year and is actively seeking postdoctoral fellows and research assistants with backgrounds in chemistry, computer science and/or bioinformatics. Contact: jihongchao@caas.cn

PI

Ji Hongchao, Associate Researcher, Master's Supervisor, Ph.D. in Science from Central South University. Joined the Shenzhen Agricultural Genomics Institute in 2022. He has received funding from the National Natural Science Foundation of China Youth Project, the Shenzhen City Outstanding Young Scientist Training Project, and other projects.

He has published over 20 papers in international academic journals such as Nature Communications, Cell Chemical Biology, Analytical Chemistry, and Briefings in Bioinformatics. He has applied for 2 national invention patents and 1 PCT international patent. He serves as a reviewer for journals such as Briefings in Bioinformatics, Journal of Chemical Information and Modeling, Analytical and Bioanalytical Chemistry, and Chemometrics and Intelligent Laboratory Systems, and guest editor for the Metabolites.

Research field

  1. Development of chemoinformatics and artificial intelligence algorithms and their application in plant metabolomics.
  2. Development of algorithms and software for the analysis of plant and crop metabolomics mass spectrometry data.
  3. Annotation of structures and functional analysis of unknown small molecule metabolites in plants and crops.

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Representative publications

  1. Ji, H.; Xu, Y.; Lu, H.; Zhang, Z. Deep MS/MS-Aided Structural-Similarity Scoring for Unknown Metabolite Identification. Anal. Chem. 2019, 91 (9), 5629–5637. link
  2. Ji, H.; Deng, H.; Lu, H.; Zhang, Z. Predicting a Molecular Fingerprint from an Electron Ionization Mass Spectrum with Deep Neural Networks. Anal. Chem. 2020, 92 (13), 8649–8653. link
  3. Yang, Q.#; Ji, H.#; Xu, Z.; Li, Y.; Wang, P.; Sun, J.; Fan, X.; Zhang, H.; Lu, H.; Zhang, Z. Ultra-Fast and Accurate Electron Ionization Mass Spectrum Matching for Compound Identification with Million-Scale in-Silico Library. Nat. Commun. 2023, 14 (1), 3722. link
  4. Ji, H.#; Lu, X.#; Zhao, S.; Wang, Q.; Bin, L.; Huber, K. V. M.; Luo, R.; Tian, R.; Tan, C. S. H. Target deconvolution with matrix-augmented pooling strategy reveals cell-specific drug-protein interactions. Cell Chem. Biol. 2023, 30(11) 1478-1487. link
  5. Ji, H.; Lu, X.; Zheng, Z.; Sun, S.; Tan, C.S.H. ProSAP: A GUI Software Tool for Statistical Analysis and Assessment of Thermal Stability Data. Brief. Bioinform. 2022, 23 (3), bbac057. link

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