• 代绍兴 (Shaoxing Dai)

    博士，教授，博士生导师

    生物信息大数据平台负责人

    Ph.D., Professor, PhD supervisor

    Principal Investigator in Bioinformatics big data platform

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    一、教育及工作经历 Education and Academic Positions

    • 2004.09-2008.06：兰州大学，生命科学学院，生物科学，理学学士
    • 2008.09-2013.06：中国科学技术大学，生物化学与分子生物学，理学博士
    • 2013.07-2018.11：中国科学院昆明动物研究所，助理研究员
    • 2018.12-2020.11：昆明理工大学灵长类转化医学研究院，讲师
    • 2020.12-2023.11：昆明理工大学灵长类转化医学研究院，副教授
    • 2023.12 - 至今：昆明理工大学灵长类转化医学研究院，教授

    • 2004.09-2008.06: School of Life Sciences, Lanzhou University, B. S. in Biological Science
    • 2008.09-2013.06: School of Life Sciences, University of Science and Technology of China (USTC), Ph. D. in Biochemistry and Molecular Biology
    • 2013.7-2018.11: Assistant researcher, Kunming Institute of Zoology, Chinese Academy of Sciences
    • 2018.12-2020.11: lecturer, Institute of Primate Translational Medicine, Kunming University of Science and Technology
    • 2020.12-2023.11: Associate professor, Institute of Primate Translational Medicine, Kunming University of Science and Technology
    • 2023.12-Now: Professor, Institute of Primate Translational Medicine, Kunming University of Science and Technology

    二、主要研究方向 Research interests

    1. 通过整合严重疾病中的多组学数据发掘和鉴定治疗靶标(系统生物学)
    2. 对治疗靶点及其药物结合口袋进行三维结构分析和可视化(结构生物信息学)
    3. 筛选和设计能够靶向和调控治疗靶点的先导化合物(药物发现)
    4. 机器学习和深度学习在转化医学中的运用

    I mainly focus on the application of bioinformatics and machine learning in target identification and drug discovery by integrating multi-omics data.

    1. Identification of therapeutic targetby integrating multi-omics data in serious diseases (System biology)
    2. Structural analysis of thetherapeutic target and its drug-binding site (Structuralbioinformatics)
    3. Screening and design the lead compounds that modulate the target (Drug discovery)
    4. Application of Machine learning and deep learning

    三、科研领域描述 Research Field

    团队长期专注于“生物信息学与药物发现”研究领域，通过综合利用生物信息学、人工智能、基因编辑、动物模型等技术体系，整合疾病多组学数据，发掘新颖的疾病靶标，在此基础上开发出能够精准调控新靶标的基因编辑疗法和小分子药物。以传统中草药和天然产物为出发点，发展了一系列针对多种疾病（传染疾病、癌症、神经退行性疾病等）和衰老的活性化合物预测方法和平台。为了提高药物研发效率，基于机器学习和深度学习以及化学信息学理论，开发了药物智能研发系统(DrugPredict)。

    The team has a long-standing focus on bioinformatics and drug discovery. Through the comprehensive use of bioinformatics, artificial intelligence, gene editing, animal models and other technology systems, the team integrates multi-omics data of diseases, discovers novel disease targets, and on this basis develops gene editing therapeutics and small molecule drugs capable of precisely regulating the new targets. Taking traditional Chinese herbs and natural products as a starting point, we have developed a series of drug prediction methods and platforms for a wide range of diseases (infectious diseases, cancer, neurodegenerative diseases, etc.) and ageing. To improve the efficiency of drug discovery and development, an intelligent drug discovery and development system (DrugPredict) has been developed based on machine learning, deep learning and the theory of chemoinformatics.

    四、承担科研项目情况 Research funding

    1. 国家自然科学基金-青年科学基金项目，2015-2017，项目名称：抗HIV新靶标的生物信息学发掘和实验验证，主持
    2. 云南省基础研究计划-面上项目，2019-2022，项目名称：利用机器学习发掘新型低毒抗细菌化合物及其实验验证，主持
    3. 云南省高层次科技人才及创新团队选拔专项，2020-2025，项目名称：技术创新人才培养对象项目代绍兴，主持
    4. 国家自然科学基金——地区科学基金项目，2022-2025，项目名称：基于机器学习和异体住囊虫模型的抗衰老化合物筛选及其机制研究，主持
    5. 中央引导地方项目专项，2022-2023，项目名称：联合人工智能和动物模型进行抗衰老化合物筛选研究，主持
    6. 云南省“兴滇英才支持计划” 产业创新人才项目，2024-2028，主持
    7. 国家自然科学基金——面上项目，2025-2028，项目名称：调控hnRNPA2B1蛋白二聚化的小分子抑制剂的计算筛选、验证和抗衰老活性研究，主持

    五、代表性论文 Publications

    1. Zhang R, Chen Z, Li S, Lv H, Li J, Yang N*, Dai S*. (2025). Proteome-Wide Identification and Comparison of Drug Pockets for Discovering New Drug Indications and Side Effects. Molecules, 30(2).
    2. Ma, L., Yan, Y., Dai, S., Shao, D., Yi, S., Wang, J., ... & Yan, J. (2024). Research on Prediction of Human Oral Bioavailability of Drugs Based on Improved Deep Forest. Journal of Molecular Graphics and Modelling, 108851.
    3. Yao J#, Dai S#, Zhu R#, Tan J, Zhao Q, Yin Y, Sun J, Du X, Ge L, Xu J, Hou C, Li N, Li J, Ji W, Zhu C*, Zhang R*, Li T*. Deciphering molecular heterogeneity and dynamics of human hippocampal neural stem cells at different ages and injury states. eLife, 2024, 12
    4. Yang T R, Huang S C, Wang Y F, Lou Z X, Dai S X, Su L H, Xu M*. Tsaokoflavanols A1-J1: Flavanol-fatty alcohol hybrids with HPL inhibitory activity from Amomum tsao-ko. Phytochemistry, 2024, 219(113982).
    5. Wu J, Kang Y, Luo X, Dai S, Shi Y, Li Z, Tang Z, Chen Z, Zhu R, Yang P, Li Z, Wang H, Chen X, Zhao Z, Ji W*, Niu Y*. Long-term in vivo chimeric cells tracking in non-human primate. Protein & Cell, 2023
    6. WeiJ#, Dai S#, Yan Y#, Li S, Yang P, ZhuR, Huang T, Li X, Duan Y, Wang Z*, Ji W*, Si W*. Spatiotemporal proteomic atlas of multiple brain regions across early fetal to neonatal stages in cynomolgus monkey. Nature communications 2023, 14(1): 3917.
    7. Wang Y F#, Zheng Y#, Cha Y Y, Feng Y, Dai S X, Zhao S*, Chen H*, XuM*. Essential oil of lemon myrtle (Backhousia citriodora) induces S-phase cell cycle arrest and apoptosis in HepG2 cells [J]. Journal of ethnopharmacology, 2023, 312(116493).
    8. Wang Y F#, Zheng Y#, Feng Y, Chen H, Dai S X, Wang Y*, Xu M*.Comparative Analysis of Active Ingredients and Potential Bioactivities of Essential Oils from Artemisia argyi and A. verlotorum [J]. Molecules, 2023, 28(9)
    9. Zuo D#, Chen Y#, Cai J P#, Yuan H Y, Wu J Q, Yin Y, Xie J W, Lin J M, Luo J, Feng Y, Ge L J, Zhou J, Quinn R J, Zhao S J, Tong X, Jin D Y, Yuan S*, Dai S X*, Xu M*. A hnRNPA2B1 agonist effectively inhibits HBV and SARS-CoV-2 omicron in vivo [J]. Protein & Cell, 2023, 14(1): 37-50
    10. Liang J, Zheng Y, Tong X, Yang N, Dai S*. In Silico Identification of Anti-SARS-CoV-2 Medicinal Plants Using Cheminformatics and Machine Learning [J]. Molecules, 2022, 28(1)
    11. Yu Kang#, Shaoxing Dai#, Yuqiang Zeng#, Fang Wang#, Pengpeng Yang, Zhaohui Yang, Youwei Pu1,3, Zifan Li, Xinglong Chen1, Baohong Tian, Wei Si, Weizhi Ji*, Yuyu Niu*. Cloning and base editing of GFP transgenic rhesus monkey and off-target analysis. Science Advances. 2022.7 8(29)：1-11 [IF: 14.957]
    12. Zhu R#, Tang J#, Xing C, Nan Q, Liang G, Luo J, Zhou J, Miao Y, Cao Y*, Dai S*, Lan D*. The Distinguishing Bacterial Features From Active and Remission Stages of Ulcerative Colitis Revealed by Paired Fecal Metagenomes [J]. Front Microbiol, 2022, 13:883495
    13. Tong X, Li W X, Liang J, Zheng Y, Dai S X*. Two different aging paths in human blood revealed by integrated analysis of gene Expression, mutation and alternative splicing [J]. Gene, 2022, 829：146501.
    14. Tong L.; Dai S. X.; Kong D. J.; Yang P. P.; Tong X.; Tong X. R.; Bi X. X.; Su Y.; Zhao Y. Q.; Liu Z. C.; The genome of medicinal leech (Whitmania pigra) and comparative genomic study for exploration of bioactive ingredients, BMC Genomics, 2022, 23(1): 76.
    15. Li W. X.; Tong X.; Yang P. P.; Zheng Y.; Liang J. H.; Li G. H.; Liu D. H.; Guan D. G.; Dai S. X*.; Screening of antibacterial compounds with novel structure from the FDA approved drugs using machine learning methods, Aging, 2022, 14(3): 1448-1472.
    16. Yang H.; Yang S.; Fan F.; Li Y.; Dai S.; Zhou X.; Steiner C. C.; Coppedge B.; Roos C.; Cai X.; Irwin D. M.; Shi P.; A New World Monkey Resembles Human in Bitter Taste Receptor Evolution and Function via a Single Parallel Amino Acid Substitution, Mol Biol Evol, 2021, 38(12): 5472-5479.
    17. Tan Tao#; Wu Jun#; Si Chenyang#; Dai Shaoxing#; Zhang Youyue; Sun Nianqin; Zhang E; Shao Honglian; Si Wei; Yang Pengpeng; Hong Wang; Zhenzhen Chen; Ran Zhu; Yu Kang; Reyna Hernandez-Benitez; Llanos Martinez Martinez; Estrella Nuñez Delicado; W Travis Berggren; May Schwarz; Zongyong Ai; Tianqing Li; Concepcion Rodriguez Esteban; Weizhi Ji*; Yuyu Niu*; Juan Carlos Izpisua Belmonte*; Chimeric contribution of human extended pluripotent stem cells to monkey embryos ex vivo, Cell, 2021, 184(8): 2020-2032. e2014.
    18. Liu Dahai#; Dai Shao-Xing#; He Kan; Li Gong-Hua; Liu Justin; Liu Leyna G; Huang Jing-Fei; Xu Lin; Li Wen-Xing; Identification of hub ubiquitin ligase genes affecting Alzheimer’s disease by analyzing transcriptome data from multiple brain regions, Science Progress, 2021, 104(1): 00368504211001146.
    19. Li Wen-Xing#; Dai Shao-Xing#; An San-Qi#; Sun Tingting#; Liu Justin; Wang Jun; Liu Leyna G; Xun Yang; Yang Hua; Fan Li-Xia; Transcriptome integration analysis and specific diagnosis model construction for Hodgkin's lymphoma, diffuse large B-cell lymphoma, and mantle cell lymphoma, Aging, 2021, 13
    20. Bimela J S, Nanfack A J, Yang P, Dai S, Kong X P, Torimiro J N, Duerr R*.Antiretroviral Imprints and Genomic Plasticity of HIV-1 pol in Non-clade B: Implications for Treatment [J]. Front Microbiol, 2021, 12(812391).
    21. Wang Fang; Zhang Weiqi; Yang Qiaoyan; Kang Yu; Fan Yanling; Wei Jingkuan; Liu Zunpeng; Dai Shaoxing; Li Hao; Li Zifan; Generation of a Hutchinson–Gilford progeria syndrome monkey model by base editing, Protein & cell, 2020, 11(11): 809-824.
    22. Li Wen-Xing; Li Gong-Hua; Tong Xin; Yang Peng-Peng; Huang Jing-Fei*; Xu Lin*; Dai Shao-Xing*; Systematic metabolic analysis of potential target, therapeutic drug, diagnostic method and animal model applicability in three neurodegenerative diseases, Aging, 2020, 12(10): 9882.
    23. Li Gong-Hua; Dai Shaoxing; Han Feifei; Li Wenxin; Huang Jingfei; Xiao Wenzhong; FastMM: an efficient toolbox for personalized constraint-based metabolic modeling, BMC Bioinformatics, 2020, 21(1): 1-7.
    24. Li Bing-xiang; Zhang Han; Liu Yubin; Li Ya; Zheng Jun-juan; Li Wen-Xing; Feng Kai; Sun Ming*; Dai Shao-Xing*; Novel pathways of HIV latency reactivation revealed by integrated analysis of transcriptome and target profile of bryostatin, Scientific Reports, 2020, 10(1): 1-12.
    25. Liu Jia-Qian; Li Wen-Xing; Zheng Jun-Juan; Tian Qing-Nan; Huang Jing-Fei*; Dai Shao-Xing*; Gain and loss events in the evolution of the apolipoprotein family in vertebrata, BMC evolutionary biology, 2019, 19(1): 1-10.
    26. Li Huijuan; Zhou Dong-Sheng; Chang Hong; Wang Lu; Liu Weipeng; Dai Shao-Xing; Zhang Chen; Cai Jun; Liu Weiqing; Li Xingxing; Interactome Analyses implicated CAMK2A in the genetic predisposition and pharmacological mechanism of Bipolar Disorder, Journal of psychiatric research, 2019, 115(165-175).
    27. Zhou Xia; Li Gonghua; An Sanqi; Li Wen-Xing; Yang Huihui; Guo Yicheng; Dai Zhi; Dai Shaoxing; Zheng Junjuan; Huang Jingfei; A new method of identifying glioblastoma subtypes and creation of corresponding animal models, Oncogene, 2018, 37(35): 4781-4791.
    28. Zheng Jun-Juan; Li Wen-Xing; Liu Jia-Qian; Guo Yi-Cheng; Wang Qian; Li Gong-Hua*Dai Shao-Xing*; Huang Jing-Fei*; Low expression of aging-related NRXN3 is associated with Alzheimer disease: A systematic review and meta-analysis, Medicine, 2018, 97(28):
    29. Li Hui‑Juan; Li Wen‑Xing; Dai Shao‑Xing; Guo Yi‑Cheng; Zheng Jun‑Juan; Liu Jia‑Qian; Wang Qian; Chen Bi‑Wen; Li Gong‑Hua; Huang Jing‑Fei; Identification of metabolism-associated genes and pathways involved in different stages of clear cell renal cell carcinoma, Oncology Letters, 2018, 15(2): 2316-2322.
    30. Chen Bi-Wen; Li Wen-Xing; Wang Guang-Hui; Li Gong-Hua; Liu Jia-Qian; Zheng Jun-Juan; Wang Qian; Li Hui-Juan; Dai Shao-Xing*; Huang Jing-Fei*; A strategy to find novel candidate anti-Alzheimer’s disease drugs by constructing interaction networks between drug targets and natural compounds in medical plants, PeerJ, 2018, 6(e4756.
    31. Wang Qian; Li Wen-Xing; Dai Shao-Xing; Guo Yi-Cheng; Han Fei-Fei; Zheng Jun-Juan; Li Gong-Hua; Huang Jing-Fei; Meta-Analysis of Parkinson’s Disease and Alzheimer’s Disease Revealed Commonly Impaired Pathways and Dysregulation of NRF2-Dependent Genes, Journal of Alzheimer's Disease, 2017, 56(4): 1525-1539.
    32. Liu Jia-Qian#; Dai Shao-Xing#; Zheng Jun-Juan; Guo Yi-Cheng; Li Wen-Xing; Li Gong-Hua; Huang Jing-Fei; The identification and molecular mechanism of anti-stroke traditional Chinese medicinal compounds, Scientific Reports, 2017, 7(41406).
    33. Li Wen-Xing; Qi Fei; Liu Jia-Qian; Li Gong-Hua; Dai Shao-Xing; Zhang Tao; Cheng Fei; Liu Dahai; Zheng Song Guo; Different impairment of immune and inflammation functions in short and long-term after ischemic stroke, American Journal of Translational Research, 2017, 9(2): 736.
    34. Li Wen-Xing; He Kan; Tang Ling; Dai Shao-Xing; Li Gong-Hua; Lv Wen-Wen; Guo Yi-Cheng; An San-Qi; Wu Guo-Ying; Liu Dahai; Comprehensive tissue-specific gene set enrichment analysis and transcription factor analysis of breast cancer by integrating 14 gene expression datasets, Oncotarget, 2017, 8(4): 6775.
    35. Li Wen-Xing; Cheng Fei; Zhang A-Jie; Dai Shao-Xing; Li Gong-Hua; Lv Wen-Wen; Zhou Tao; Zhang Qiang; Zhang Hong; Zhang Tao; Folate deficiency and gene polymorphisms of MTHFR, MTR and MTRR elevate the hyperhomocysteinemia risk, Clin Lab, 2017, 63(3): 523-533.
    36. Li Wen-Xing#; Dai Shao-Xing#; Wang Qian; Guo Yi-Cheng; Hong Yi; Zheng Jun-Juan; Liu Jia-Qian; Liu Dahai; Li Gong-Hua; Huang Jing-Fei; Integrated analysis of ischemic stroke datasets revealed sex and age difference in anti-stroke targets, PeerJ, 2016, 4(e2470).
    37. Li Wen-Xing#; Dai Shao-Xing#; Liu Jia-Qian; Wang Qian; Li Gong-Hua; Huang Jing-Fei; Integrated Analysis of Alzheimer’s Disease and Schizophrenia Dataset Revealed Different Expression Pattern in Learning and Memory, Journal of Alzheimer's Disease, 2016, 51(2): 417-425.
    38. Han Feifei; Li Gonghua; Dai Shaoxing; Huang Jingfei; Genome-wide metabolic model to improve understanding of CD4+ T cell metabolism, immunometabolism and application in drug design, Molecular BioSystems, 2016, 12(2): 431-443.
    39. Guo Yi-Cheng; Zhang Lin; Dai Shao-Xing; Li Wen-Xing; Zheng Jun-Juan; Li Gong-Hua; Huang Jing-Fei; Independent evolution of winner traits without whole genome duplication in Dekkera yeasts, PLOS ONE, 2016, 11(5): e0155140.
    40. Dai Shao-Xing; Li Wen-Xing; Li Gong-Hua; Huang Jing-Fei; Proteome-wide prediction of targets for aspirin: new insight into the molecular mechanism of aspirin, PeerJ, 2016, 4(e1791).
    41. Dai Shao-Xing; Li Wen-Xing; Han Fei-Fei; Guo Yi-Cheng; Zheng Jun-Juan; Liu Jia-Qian; Wang Qian; Gao Yue-Dong; Li Gong-Hua; Huang Jing-Fei; In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database, Scientific Reports, 2016, 6(1): 1-11.
    42. Dai Shao-Xing; Li Gong-Hua; Gao Yue-Dong; Huang Jing-Fei; Pharmacophore-Map-Pick: A Method to Generate Pharmacophore Models for All Human GPCRs, Molecular Informatics, 2016, 35(2): 81-91.
    43. Li Wen-Xing; Lv Wen-Wen; Dai Shao-Xing; Pan Ming-Luo; Huang Jing-Fei; Joint associations of folate, homocysteine and MTHFR, MTR and MTRR gene polymorphisms with dyslipidemia in a Chinese hypertensive population: a cross-sectional study, Lipids in health and disease, 2015, 14(1): 1.
    44. Li Wen-Xing#; Dai Shao-Xing#; Zheng Jun-Juan; Liu Jia-Qian; Huang Jing-Fei; Homocysteine Metabolism Gene Polymorphisms (MTHFR C677T, MTHFR A1298C, MTR A2756G and MTRR A66G) Jointly Elevate the Risk of Folate Deficiency, Nutrients, 2015, 7(8): 6670-6687.
    45. Jiang Hong-Yan; Li Guo-Dong; Dai Shao-Xing; Bi Rui; Zhang Deng-Feng; Li Zong-Fang; Xu Xiu-Feng; Zhou Tai-Cheng; Yu Li; Yao Yong-Gang; Identification of PSEN1 mutations p. M233L and p. R352C in Han Chinese families with early-onset familial Alzheimer's disease, Neurobiology of aging, 2015, 36(3): 1602. e1603-1602. e1606.
    46. Wang Rui-Rui; Yang Qing-Hua; Luo Rong-Hua; Peng You-Mei; Dai Shao-Xing; Zhang Xing-Jie; Chen Huan; Cui Xue-Qing; Liu Ya-Juan; Huang Jing-Fei; Azvudine, a novel nucleoside reverse transcriptase inhibitor showed good drug combination features and better inhibition on drug-resistant strains than lamivudine in vitro, PLOS ONE, 2014, 9(8): e105617.
    47. Zhang Ai-Di; Dai Shao-Xing; Huang Jing-Fei; Reconstruction and analysis of human kidney-specific metabolic network based on omics data, BioMed research international, 2013
    48. Li Zhonghua; Li Tiehai; Dai Shaoxing; Xie Xiaoli; Ma Xiaofeng; Zhao Wei; Zhang Weimin; Li Jing; Wang Peng George; New Insights into the Pharmacological Chaperone Activity of C2-Substituted Glucoimidazoles for the Treatment of Gaucher Disease, ChemBioChem, 2013, 14(10): 1239-1247.
    49. Dai Shao-Xing; Zhang Ai-Di; Huang Jing-Fei; Evolution, expansion and expression of the Kunitz/BPTI gene family associated with long-term blood feeding in Ixodes Scapularis, BMC evolutionary biology, 2012, 12(1): 1-16.

    论文网站(动态更新) https://scholar.google.com/citations?user=d4cYz1YAAAAJ

    六、申请专利 Patents

    1. 代绍兴; 吕豪豪; 包杨欢; 杨鹏鹏; 周子昂. 一种抗抑郁药物预测方法. CN202411430075.3 (公布)
    2. 代绍兴; 朱正娜; 许敏; 吴培荣; 贾荣芳. 一种有机蜡菊/永久花精油在制备抗衰老活性产品中的应用. CN202311463056.6 (公布)
    3. 代绍兴; 贾荣芳; 许敏; 吴培荣; 朱正娜. 种具有抗衰老活性的有机杜松果精油及其应用. CN202311463057.0 (公布)
    4. 代绍兴; 郑阳; 梁积浩. 一种抗衰老化合物数据库、构建方法及应用. CN202310271262.0 (公布)
    5. 代绍兴; 梁积浩; 郑阳. 一种针对多种病毒的新型活性化合物计算筛选方法. CN202310271258.4 (公布)
    6. 许敏; 代绍兴. 一种倍半萜类衍生物及其在制备广谱抗病毒药物中的应用. CN202210291346.6 (已授权)
    7. 代绍兴; 仝新; 姜辰龙; 黄京飞; 赖仞; 李文兴; 李功华; 梁积浩; 郑阳; 杨鹏鹏. 达舒平的抗细菌应用及抗细菌活性预测和结构新颖性评价方法. CN202111293528.9 (公布)
    8. 仝新; 代绍兴; 姜辰龙; 黄京飞; 赖仞; 李文兴; 李功华; 梁积浩; 郑阳; 杨鹏鹏. 福辛普利的抗细菌应用及抗细菌活性预测和结构新颖性评价方法. CN202111293485.4 (公布)
    9. 代绍兴, 仝新, 姜辰龙, 黄京飞, 赖仞, 李文兴, 李功华, 梁积浩, 郑阳, 杨鹏鹏. 瑞格列奈的抗细菌应用及抗细菌活性预测和结构新颖性评价方法. ZL202111293501.X. 2023 (已授权)
    10. 代绍兴, 黄京飞, 陈欢,郑永唐, 李功华, 李文兴. 达巴万星在制备治疗艾滋病的药物中的应用. ZL201610964407.5. 2017.（已授权）
    11. 代绍兴, 黄京飞, 陈欢,郑永唐, 李功华, 李文兴. 西曲瑞克在制备治疗艾滋病的药物中的应用. ZL201610961934.0. 2017.（已授权）
    12. 代绍兴, 黄京飞, 陈欢,郑永唐, 李功华, 李文兴. 蒽环类化合物在制备治疗艾滋病的药物中的应用. CN201610964409.4. 2017.
    13. 李功华, 黄京飞, 李文兴, 代绍兴, 唐承薇, 童欢. 肝硬化早期小分子标志物及其应用. ZL201810548561.3. 2018.（已授权）
    14. 李文兴, 李功华, 黄京飞, 赵旭东, 代绍兴. 一种基于多基因表达特征谱的结肠癌个性化预后评估方法. ZL201810440932.6. 2018.（已授权）
    15. 李文兴, 李功华, 黄京飞, 赵旭东, 代绍兴. 一种基于多基因表达特征谱的肺腺癌个性化预后评估方法. ZL201810440855.4. 2018.（已授权）
    16. 黄京飞, 李文兴, 李功华, 赵旭东, 代绍兴. 一种基于多基因表达特征谱的胃癌个性化预后评估方法. ZL201810440931.1. 2018.（已授权）
    17. 李文兴, 李功华, 黄京飞, 赵旭东, 代绍兴. 一种基于多基因表达特征谱的宫颈癌个性化预后评估方法. ZL201810440007.3. 2018.（已授权）
    18. 李文兴, 李功华, 黄京飞, 赵旭东, 代绍兴. 一种基于多基因表达特征谱的肝癌个性化预后评估方法. ZL201810440101.9. 2018.（已授权）
    19. 李功华, 李文兴, 黄京飞, 赵旭东, 代绍兴. 一种基于多基因表达特征谱的胰腺癌个性化预后评估方法. ZL201810440131.X. 2018.（已授权）
    20. 李文兴, 李功华, 黄京飞, 赵旭东, 代绍兴. 一种基于多基因表达特征谱的肾透明细胞癌个性化预后评估方法. ZL201810440933.0. 2018.（已授权）
    21. 赵旭东, 李文兴, 李功华, 黄京飞, 代绍兴. 一种基于多基因表达特征谱的胶质母细胞瘤个性化预后评估方法. ZL201810440854.X. 2018.（已授权）

    七、实验室成员

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    博士，副教授

    主要研究方向：

    1. 免疫调控在发育过程及衰老相关疾病中的作用
    2. 复杂疾病的遗传学和生物信息学研究
    3. 高通量组学大数据的整合分析与数据挖掘工具开发
    4. 机器学习和深度学习在生物大数据挖掘上的应用
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    博士，讲师

    主要研究方向：

    1. 灵长类多组学数据库建构
    2. 发育障碍类疾病病因研究

    博士研究生

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    硕士研究生

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    已毕业研究生

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    工作人员

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    欢迎优秀的本科生和硕士报考本实验室

    实验室围绕生物信息学与药物发现、免疫与衰老，常年招聘博士后、讲师、副教授等岗位

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