{"id":663,"date":"2025-10-08T00:00:41","date_gmt":"2025-10-07T16:00:41","guid":{"rendered":"http:\/\/cnhupo.org.cn\/?post_type=council_member&#038;p=663"},"modified":"2026-04-22T11:07:05","modified_gmt":"2026-04-22T03:07:05","slug":"liangqiao","status":"publish","type":"council_member","link":"http:\/\/cnhupo.org.cn\/zh\/council-member\/liangqiao\/","title":{"rendered":"\u4e54\u4eae"},"content":{"rendered":"<p>\u4e3b\u8981\u4ece\u4e8b\u86cb\u767d\u8d28\u7ec4\u5b66\u3001\u5fae\u751f\u7269\u8d28\u8c31\u5206\u6790\u3001\u5fae\u6d41\u63a7\u8d28\u8c31\u8054\u7528\u3001\u8d28\u8c31\u751f\u7269\u4fe1\u606f\u5b66\u7b49\u65b9\u9762\u7684\u57fa\u7840\u548c\u5e94\u7528\u7814\u7a76\u3002\u5148\u540e\u627f\u62c5\u548c\u53c2\u4e0e\u56fd\u5bb6\u53ca\u7701\u90e8\u7ea7\u9879\u76ee\uff0c\u5982\u56fd\u5bb6\u91cd\u70b9\u7814\u53d1\u8ba1\u5212\u3001\u56fd\u5bb6\u81ea\u7136\u79d1\u5b66\u57fa\u91d1\u91cd\u70b9\u9879\u76ee\u3001\u9762\u4e0a\u9879\u76ee\u3001\u4e0a\u6d77\u5e02\u79d1\u59d4\u79d1\u6280\u521b\u65b0\u91cd\u70b9\u4e13\u9879\u7b4921\u9879\u3002\u8fc4\u4eca\u5728Nature Machine Intelligence\u3001PNAS\u3001Nature Communications\u3001Chem\u3001JACS\u3001Angew. Chem. Int. Ed.\u3001Anal. Chem.\u7b49\u6743\u5a01\u671f\u520a\u4e0a\u53d1\u8868\u8bba\u6587160\u7bc7\uff1b\u7533\u62a5\u53ca\u83b7\u5f97\u56fd\u9645\u3001\u56fd\u5185\u53d1\u660e\u4e13\u522921\u9879\u3002\u4e3b\u8981\u7814\u7a76\u65b9\u5411\u5305\u62ec\uff1a1. \u8d28\u8c31\u548c\u86cb\u767d\u8d28\u7ec4\u6570\u636e\u5206\u6790\u7b97\u6cd5\u5f00\u53d1\uff1a(1) \u57fa\u4e8e\u8d28\u8c31\u7684\u7ec6\u83cc\u9274\u5b9a\u7b97\u6cd5\uff1b(2) \u57fa\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u86cb\u767d\u8d28\u7ec4\u6570\u636e\u5206\u6790\u7b97\u6cd5\u30022. \u5fae\u751f\u7269\u5b8f\u86cb\u767d\u8d28\u7ec4\u30023. \u57fa\u4e8e\u591a\u7ec4\u5b66\u7684\u7ec6\u83cc\u8010\u836f\u673a\u5236\u7814\u7a76\u3002<\/p>\n<p>1. Y. Zong, Y. Wang, X. Qiu, X. Huang, L. Qiao*, Deep Learning Prediction of Glycopeptide Tandem Mass Spectra Powers Glycoproteomics, Nature Machine Intelligence, 2024, DOI: 10.1038\/s42256-024-00875-x<br \/>\n2. Y. Zong, Y. Wang, Y. Yang, D. Zhao, X. Wang, C. Shen, L. Qiao*, DeepFLR Facilitates False Localization Rate Control in Phosphoproteomics, Nature Communications, 2023, 14, 2269<br \/>\n3. Y. Yang#, G. Yan, S. Kong, M. Wu, P. Yang, W. Cao#,*, L. Qiao*, GproDIA enables data-independent acquisition glycoproteomics with comprehensive statistical control, Nature Communications, 12(2021): 6073<br \/>\n4. Y. Yang, X. Liu, C. Shen, Y. Lin, P. Yang, L. Qiao*, In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics, Nature Communications, 11(2020): 146<br \/>\n5. E. Wu#, V. Mallawaarachchi#, J. Zhao, Y. Yang, H. Liu, X. Wang, C. Shen, Y. Lin, L. Qiao*, Contigs directed gene annotation (ConDiGA) for accurate protein sequence database construction in metaproteomics, Microbiome, 2024, 12: 58<\/p>","protected":false},"featured_media":662,"template":"","member_category":[],"class_list":["post-663","council_member","type-council_member","status-publish","has-post-thumbnail","hentry"],"acf":[],"_links":{"self":[{"href":"http:\/\/cnhupo.org.cn\/zh\/wp-json\/wp\/v2\/council_member\/663","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/cnhupo.org.cn\/zh\/wp-json\/wp\/v2\/council_member"}],"about":[{"href":"http:\/\/cnhupo.org.cn\/zh\/wp-json\/wp\/v2\/types\/council_member"}],"wp:featuredmedia":[{"embeddable":true,"href":"http:\/\/cnhupo.org.cn\/zh\/wp-json\/wp\/v2\/media\/662"}],"wp:attachment":[{"href":"http:\/\/cnhupo.org.cn\/zh\/wp-json\/wp\/v2\/media?parent=663"}],"wp:term":[{"taxonomy":"member_category","embeddable":true,"href":"http:\/\/cnhupo.org.cn\/zh\/wp-json\/wp\/v2\/member_category?post=663"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}