{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:55:01Z","timestamp":1757620501657,"version":"3.44.0"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819506972"},{"type":"electronic","value":"9789819506989"}],"license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-0698-9_7","type":"book-chapter","created":{"date-parts":[[2025,7,31]],"date-time":"2025-07-31T07:27:35Z","timestamp":1753946855000},"page":"72-85","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["TF-GCNNovo: A Peptide Sequence Prediction Model Integrating Transformer and\u00a0Graph Convolutional Network"],"prefix":"10.1007","author":[{"given":"Nan","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Jing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaotian","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3929-4128","authenticated-orcid":false,"given":"Binhai","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,1]]},"reference":[{"issue":"25","key":"7_CR1","first-page":"3309","volume":"66","author":"H-K Chen","year":"2021","unstructured":"Chen, H.-K., Hao, R., Tian, R.-J.: Progress in protein sequencing technology. Sci. Bull. 66(25), 3309\u20133317 (2021)","journal-title":"Sci. Bull."},{"key":"7_CR2","doi-asserted-by":"crossref","unstructured":"Gao, T., Chen, B., Mi, Q.: A survey of Markov model in reinforcement learning. In: 2022 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 284\u2013287. IEEE (2022)","DOI":"10.1109\/ICAIIC54071.2022.9722618"},{"issue":"8","key":"7_CR3","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1093\/bioinformatics\/16.8.665","volume":"16","author":"R Giegerich","year":"2000","unstructured":"Giegerich, R.: A systematic approach to dynamic programming in bioinformatics. Bioinformatics 16(8), 665\u2013677 (2000)","journal-title":"Bioinformatics"},{"issue":"31","key":"7_CR4","doi-asserted-by":"publisher","first-page":"8247","DOI":"10.1073\/pnas.1705691114","volume":"114","author":"N-T Hieu","year":"2017","unstructured":"Hieu, N.-T., Xianglilan, Z., Lei, X., et al.: De novo peptide sequencing by deep learning. Proc. Natl. Acad. Sci. U.S.A. 114(31), 8247\u20138252 (2017)","journal-title":"Proc. Natl. Acad. Sci. U.S.A."},{"issue":"8","key":"7_CR5","doi-asserted-by":"publisher","first-page":"621","DOI":"10.2174\/1574893618666230505103556","volume":"18","author":"E Ispano","year":"2023","unstructured":"Ispano, E., Lavezzo, E., Bianca, F., et al.: An overview of protein function prediction methods: a deep learning perspective. Curr. Bioinform. 18(8), 621\u2013630 (2023)","journal-title":"Curr. Bioinform."},{"key":"7_CR6","doi-asserted-by":"crossref","unstructured":"Zhong, H.-G., Xiao, L., Jia, X.-C., et al.: Hierarchical graph transformer with contrastive learning for protein function prediction. Bioinformatics (Oxford, England) 39(7) (2023)","DOI":"10.1093\/bioinformatics\/btad410"},{"key":"7_CR7","doi-asserted-by":"publisher","first-page":"107065","DOI":"10.1016\/j.compbiomed.2023.107065","volume":"162","author":"Z-W Duan","year":"2023","unstructured":"Duan, Z.-W., Xin, F., Kai, L., et al.: Identification of SH2 domain-containing proteins and motifs prediction by a deep learning method. Comput. Biol. Med. 162, 107065\u2013107065 (2023)","journal-title":"Comput. Biol. Med."},{"issue":"4","key":"7_CR8","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1021\/ac048788h","volume":"7","author":"A Frank","year":"2005","unstructured":"Frank, A., Pevzner, P.: PepNovo: de novo peptide sequencing via probabilistic network modeling. Anal. Chem. 7(4), 964\u2013973 (2005)","journal-title":"Anal. Chem."},{"key":"7_CR9","first-page":"27","volume":"104","author":"G Chakradhar","year":"2023","unstructured":"Chakradhar, G., Adrita, D., Parisa, M., et al.: PeptideBERT: a language model based on transformers for peptide property prediction. J. Phys. Chem. Lett. 104, 27\u201334 (2023)","journal-title":"J. Phys. Chem. Lett."},{"issue":"11","key":"7_CR10","doi-asserted-by":"publisher","first-page":"1250","DOI":"10.1038\/s42256-023-00738-x","volume":"5","author":"M Zeping","year":"2023","unstructured":"Zeping, M., Ruixue, Z., Lei, X., et al.: Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model. Nat. Mach. Intell. 5(11), 1250\u20131260 (2023)","journal-title":"Nat. Mach. Intell."},{"key":"7_CR11","doi-asserted-by":"crossref","unstructured":"Gilmer, J., Schoenholz, S.S., Riley, P.F., et al.: Message passing neural networks. In: Machine Learning Meets Quantum Physics, pp. 199\u2013214 (2020)","DOI":"10.1007\/978-3-030-40245-7_10"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Wu, R., Zhang, X., Wang, R., et al.: Denovo-GCN: de novo peptide sequencing by graph convolutional neural networks. Appl. Sci. 13(7) (2023)","DOI":"10.3390\/app13074604"},{"key":"7_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-018-2364-2","volume":"19","author":"X Liu","year":"2018","unstructured":"Liu, X., Yang, Z., Sang, S., et al.: Identifying protein complexes based on node embeddings obtained from protein-protein interaction networks. BMC Bioinform. 19, 1\u201314 (2018)","journal-title":"BMC Bioinform."},{"key":"7_CR14","unstructured":"Sutskever, I., Vinyals, O., Le, V. Q.: Sequence to sequence learning with neural networks. CoRR abs\/1409.3215 (2014)"},{"key":"7_CR15","unstructured":"Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding by generative pre-training. OpenAI (2018)"},{"issue":"3\u20134","key":"7_CR16","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1089\/106652799318300","volume":"6","author":"V Dancik","year":"1999","unstructured":"Dancik, V., Addona, T.A., Clauser, K.R., et al.: De novo peptide sequencing via tandem mass spectrometry. J. Comput. Biol. 6(3\u20134), 327\u2013342 (1999)","journal-title":"J. Comput. Biol."},{"key":"7_CR17","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.jbiotec.2017.05.016","volume":"261","author":"J Pfeuffer","year":"2017","unstructured":"Pfeuffer, J., Sachsenberg, T., Alka, O., et al.: OpenMS\u2013a platform for reproducible analysis of mass spectrometry data. J. Biotechnol. 261, 142\u2013148 (2017)","journal-title":"J. Biotechnol."},{"key":"7_CR18","unstructured":"Holtzman, A., Buys, J., Du, L., et al.: The curious case of neural text degeneration. arXiv preprint arXiv:1904.09751 (2019)"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Martens, L., Chambers, M., Sturm, M., et al.: mzML\u2014a community standard for mass spectrometry data. Mol. Cell. Proteomics 10(1) (2011)","DOI":"10.1074\/mcp.R110.000133"},{"issue":"2","key":"7_CR20","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1007\/s13361-012-0516-6","volume":"24","author":"AA Goloborodko","year":"2013","unstructured":"Goloborodko, A.A., Levitsky, L.I., Ivanov, M.V., et al.: Pyteomics\u2013a Python framework for exploratory data analysis and rapid software prototyping in proteomics. J. Am. Soc. Mass Spectrom. 24(2), 301\u2013304 (2013)","journal-title":"J. Am. Soc. Mass Spectrom."},{"key":"7_CR21","doi-asserted-by":"crossref","unstructured":"Perez-Riverol, Y., Wang, R., Hermjakob, H., et al.: Open source libraries and frameworks for mass spectrometry based proteomics: a developer\u2019s perspective. Biochimica et Biophysica Acta (BBA)-Proteins Proteomics 1844(1), 63\u201376 (2014)","DOI":"10.1016\/j.bbapap.2013.02.032"}],"container-title":["Lecture Notes in Computer Science","Bioinformatics Research and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-0698-9_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T08:28:06Z","timestamp":1757320086000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-0698-9_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,1]]},"ISBN":["9789819506972","9789819506989"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-0698-9_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,8,1]]},"assertion":[{"value":"1 August 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISBRA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Bioinformatics Research and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Helsinki","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Finland","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":"3 August 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 August 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":"isbra2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.helsinki.fi\/en\/conferences\/isbra2025","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}