{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T00:54:13Z","timestamp":1774659253013,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819500291","type":"print"},{"value":"9789819500307","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-95-0030-7_33","type":"book-chapter","created":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T07:35:43Z","timestamp":1753342543000},"page":"383-394","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["E-MSNGO: Explainable Multi-species Protein Function Prediction Model Based on Aggregated Networks"],"prefix":"10.1007","author":[{"given":"Beibei","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siyuan","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiqu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junyi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,25]]},"reference":[{"key":"33_CR1","doi-asserted-by":"publisher","first-page":"i262","DOI":"10.1093\/bioinformatics\/btab270","volume":"37","author":"R You","year":"2021","unstructured":"You, R., Yao, S., Mamitsuka, H., Zhu, S.: DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction. Bioinformatics 37, i262\u2013i271 (2021)","journal-title":"Bioinformatics"},{"key":"33_CR2","doi-asserted-by":"publisher","first-page":"1050","DOI":"10.1038\/s42256-021-00419-7","volume":"3","author":"M Torres","year":"2021","unstructured":"Torres, M., Yang, H., Romero, A.E., Paccanaro, A.: Protein function prediction for newly sequenced organisms. Nat. Mach. Intell. 3, 1050\u20131060 (2021)","journal-title":"Nat. Mach. Intell."},{"key":"33_CR3","doi-asserted-by":"crossref","unstructured":"Yuan, Q., Xie, J., Xie, J., Zhao, H., Yang, Y.: Fast and accurate protein function prediction from sequence through pretrained language model and homology-based label diffusion. Brief Bioinform 24 (2023)","DOI":"10.1093\/bib\/bbad117"},{"key":"33_CR4","doi-asserted-by":"publisher","first-page":"1713","DOI":"10.1109\/TCBB.2022.3215257","volume":"20","author":"K Wu","year":"2023","unstructured":"Wu, K., Wang, L., Liu, B., Liu, Y., Wang, Y., Li, J.: PSPGO: cross-species heterogeneous network propagation for protein function prediction. IEEE\/ACM Trans. Comput. Biol. Bioinform. 20, 1713\u20131724 (2023)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"33_CR5","doi-asserted-by":"publisher","first-page":"i238","DOI":"10.1093\/bioinformatics\/btac256","volume":"38","author":"M Kulmanov","year":"2022","unstructured":"Kulmanov, M., Hoehndorf, R.: DeepGOZero: improving protein function prediction from sequence and zero-shot learning based on ontology axioms. Bioinformatics 38, i238\u2013i245 (2022)","journal-title":"Bioinformatics"},{"key":"33_CR6","doi-asserted-by":"crossref","unstructured":"Zhang, X., et al.: HNetGO: protein function prediction via heterogeneous network transformer. Brief Bioinform 24 (2023)","DOI":"10.1093\/bib\/bbab556"},{"key":"33_CR7","doi-asserted-by":"crossref","unstructured":"Jiao, P., Wang, B., Wang, X., Liu, B., Wang, Y., Li, J.: Struct2GO: protein function prediction based on graph pooling algorithm and AlphaFold2 structure information. Bioinformatics 39 (2023)","DOI":"10.1093\/bioinformatics\/btad637"},{"key":"33_CR8","doi-asserted-by":"crossref","unstructured":"Wang, B., Cui, B., Chen, S., Wang, X., Wang, Y., Li, J.: MSNGO: multi-species protein function annotation based on 3D protein structure and network propagation. arXiv preprint arXiv:2503.23014 (2025)","DOI":"10.1093\/bioinformatics\/btaf285"},{"key":"33_CR9","unstructured":"Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"33_CR10","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201c Why should I trust you?\u201d Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144 (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"33_CR11","unstructured":"Ying, R., Bourgeois, D., You, J., Zitnik, M., Leskovec, J.: GNNExplainer: generating explanations for graph neural networks. In: Advances in Neural Information Processing Systems, vol. 32, pp. 9240-9251 (2019)"},{"key":"33_CR12","doi-asserted-by":"crossref","unstructured":"Pfeifer, B., Saranti, A., Holzinger, A.: GNN-SubNet: disease subnetwork detection with explainable graph neural networks. Bioinformatics 38, ii120-ii126 (2022)","DOI":"10.1093\/bioinformatics\/btac478"},{"key":"33_CR13","doi-asserted-by":"publisher","first-page":"e51","DOI":"10.1093\/nar\/gkab044","volume":"49","author":"Y Xia","year":"2021","unstructured":"Xia, Y., Xia, C.Q., Pan, X., Shen, H.B.: GraphBind: protein structural context embedded rules learned by hierarchical graph neural networks for recognizing nucleic-acid-binding residues. Nucleic Acids Res. 49, e51 (2021)","journal-title":"Nucleic Acids Res."},{"key":"33_CR14","doi-asserted-by":"crossref","unstructured":"Chiang, Y., Hui, W.-H., Chang, S.-W.: Encoding protein dynamic information in graph representation for functional residue identification. Cell Rep. Phys. Sci. 3 (2022)","DOI":"10.1016\/j.xcrp.2022.100975"},{"key":"33_CR15","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.1038\/s41598-020-80786-0","volume":"11","author":"M Littmann","year":"2021","unstructured":"Littmann, M., Heinzinger, M., Dallago, C., Olenyi, T., Rost, B.: Embeddings from deep learning transfer GO annotations beyond homology. Sci. Rep. 11, 1160 (2021)","journal-title":"Sci. Rep."},{"key":"33_CR16","doi-asserted-by":"crossref","unstructured":"Szklarczyk, D., et al.: STRING v11: protein\u2013protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607-D613 (2018)","DOI":"10.1093\/nar\/gky1131"},{"key":"33_CR17","doi-asserted-by":"publisher","first-page":"D439","DOI":"10.1093\/nar\/gkab1061","volume":"50","author":"M Varadi","year":"2022","unstructured":"Varadi, M., et al.: AlphaFold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 50, D439\u2013D444 (2022)","journal-title":"Nucleic Acids Res."},{"key":"33_CR18","doi-asserted-by":"publisher","first-page":"D1057","DOI":"10.1093\/nar\/gku1113","volume":"43","author":"RP Huntley","year":"2015","unstructured":"Huntley, R.P., et al.: The GOA database: gene ontology annotation updates for 2015. Nucleic Acids Res. 43, D1057\u2013D1063 (2015)","journal-title":"Nucleic Acids Res."},{"key":"33_CR19","first-page":"855","volume":"2016","author":"A Grover","year":"2016","unstructured":"Grover, A., Leskovec, J.: Node2vec: scalable feature learning for networks. KDD 2016, 855\u2013864 (2016)","journal-title":"KDD"},{"key":"33_CR20","doi-asserted-by":"publisher","first-page":"e2016239118","DOI":"10.1073\/pnas.2016239118","volume":"118","author":"A Rives","year":"2021","unstructured":"Rives, A., et al.: Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Proc. Natl. Acad. Sci. 118, e2016239118 (2021)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"33_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, P., et al.: TransGNN: harnessing the collaborative power of transformers and graph neural networks for recommender systems. In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1285\u20131295 (2023)","DOI":"10.1145\/3626772.3657721"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-0030-7_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T00:11:24Z","timestamp":1774656684000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-0030-7_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819500291","9789819500307"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-0030-7_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"25 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}},{"value":"All additional files including supplemental materials, data and code are available at:\n                      \n                      .","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code Files"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}