主要学术成果 多年致力于蛋白质磷酸化修饰进化、网络、与疾病相关性研究，以及多种组学的整合研究。目前主要从事生物大分子修饰及其功能研究，在nature chemical biology, nature communications, genome research、cell reports和genome bioformatics proteomics等sci期刊上发表多篇论文。先后主持国家基金委面上基金和山东省面上基金等项目。 科研项目： 1. 国家自然科学基金面上项目（32071430）58万， 2021-2024； 2. 国家自然科学基金面上项目（31770821）60万，2018-2021； 3. 山东省自然科学基金面上项目（zr2016cm14）18万， 2016-1018; 著作教材： 无 期刊论文：（#：共同第一作者；*：共通讯作者） 大分子修饰位点预测算法开发: 1. lyu x#, li s#, jiang c, he n, chen z, zou y*, li l*. deepcso: a deep-learning network approach to predicting cysteine s-sulphenylation sites.frontiers in cell and developmental biology. 2020 8: 594587. （中科院二区） 2. zhang l#, zou y#, he n, chen y, chen z*, li l*. deepkhib: a deep-learning framework for lysine 2-hydroxyisobutyrylation sites prediction.frontiers in cell and developmental biology. 2020 8: 580217. （中科院二区） 3. zhao y, he n, chen z*, li l*. identification of protein lysine crotonylation sites by a deep learning framework with convolutional neural networks.ieee access. 2020 8: 14244-14252. （中科院二区） 4. chen z, he n, huang y, qin wt, liu x*, li l*. integration of a deep learning classifier with a random forest approach for predicting malonylation sites.genomics proteomics bioinformatics. 2018 dec;16(6):451-459.（中科院一区） 5. huang y#, he n#, chen y, chen z*, li l*. bermp: a cross-species classifier for predicting m6a sites by integrating a deep learning algorithm and a random forest approach.international journal of biological sciences. 2018 sep 7;14(12):1669-1677.（中科院二区） 6. chen z#, liu x#, li f, li c, marquez-lago t, leier a, akutsu t, webb gi, xu d, smith ai, li l*, chou kc*, song j*. large-scale comparative assessment of computational predictors for lysine post-translational modification sites. briefings in bioinformatics. 2018 oct 4.（中科院一区） 噬菌体展示展示技术研制抗体及相关技术和工具开发 7. li s, zou y, zhao d, yin y, song j, he n, liu h, qian d*, li l*, huang h*. revisiting the phosphotyrosine binding pocket of fyn sh2 domain led to the identification of novel sh2 superbinders.protein science. 2021. mar;30(3):558-570. （中科院三区） 大分子修饰特性、疾病相关研究及工具开发 8. ming l#, zou y#, zhao y, zhang l, he n, chen z, li ss* ,li l*. mms2plot: an r package for visualizing multiple ms/ms spectra for groups of modified and non-modified peptides.proteomics. 2020 e2000061.（中科院三区） 9. bian y#, li l#, dong m#, liu x, kaneko t, cheng k, liu h, voss c, cao x, wang y, litchfield d, ye m*, li ss*, zou h*, ultradeep tyrosine phosphoproteomics enabled by a phosphotyrosine superbinder.nature chemical biology. 2016 sep 19. (nature子刊) 10. li l, wei y, to c, zhu cq, tong j, pham na, taylor p, ignatchenko v, ignatchenko a, zhang w, wang d, yanagawa n, li m, pintilie m, liu g, muthuswamy l, shepherd fa, tsao ms, kislinger t, moran mf*. integrated omic analysis of lung cancer reveals metabolism proteome signatures with prognostic impact.nature communications. 2014 nov 28;5:5469.（nature子刊） 11. zaman n#, li l#, jaramillo ml, sun z, tibiche c, banville m, collins c, trifiro m, paliouras m, nantel a, o'connor-mccourt m, wang e*. signaling network assessment of mutations and copy number variations predict breast cancer subtype-specific drug targets. cell reports 2013 oct 17;5(1):216-23.（cell子刊） 12. li l#, tibiche c#, fu c, kaneko t, moran mf, schiller mr, li ss*, wang e*. the human phosphotyrosine signaling network: evolution and hotspots of hijacking in cancer.genome research. 2012 jul;22(7):1222-30.（中科院一区） 获奖情况： 山东省泰山学者海外特聘专家（2014） |
