{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T16:05:12Z","timestamp":1784217912215,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":39,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819203680","type":"print"},{"value":"9789819203697","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-981-92-0369-7_2","type":"book-chapter","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T11:16:59Z","timestamp":1778498219000},"page":"19-35","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Cross-Modal Hierarchical Contrastive Learning Framework for\u00a0Protein-Protein Interaction Prediction"],"prefix":"10.1007","author":[{"given":"Ran","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuezhi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"QingQing","family":"Long","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianghua","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,12]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Bi, X., Ma, W., Jiang, H., Lu, W., Wei, Z., Zhang, S.: SSPPI: cross-modality enhanced protein\u2013protein interaction prediction from sequence and structure perspectives. IEEE Trans. Neural Netw. Learn. Syst. (2025)","DOI":"10.1109\/TNNLS.2025.3599927"},{"issue":"6","key":"2_CR2","doi-asserted-by":"publisher","first-page":"2763","DOI":"10.3390\/ijms10062763","volume":"10","author":"A Br\u00fcckner","year":"2009","unstructured":"Br\u00fcckner, A., Polge, C., Lentze, N., Auerbach, D., Schlattner, U.: Yeast two-hybrid, a powerful tool for systems biology. Int. J. Mol. Sci. 10(6), 2763\u20132788 (2009)","journal-title":"Int. J. Mol. Sci."},{"issue":"14","key":"2_CR3","doi-asserted-by":"publisher","first-page":"i305","DOI":"10.1093\/bioinformatics\/btz328","volume":"35","author":"M Chen","year":"2019","unstructured":"Chen, M., et al.: Multifaceted protein-protein interaction prediction based on Siamese residual RCNN. Bioinformatics 35(14), i305\u2013i314 (2019)","journal-title":"Bioinformatics"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Chen, Z., et al.: Genesum: large language model-based gene summary extraction. In: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1438\u20131443. IEEE (2024)","DOI":"10.1109\/BIBM62325.2024.10822279"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Cui, W., et al.: Refining computational inference of gene regulatory networks: integrating knockout data within a multi-task framework. Brief. Bioinform. 25(5), bbae361\u2013bbae361 (2024)","DOI":"10.1093\/bib\/bbae361"},{"key":"2_CR6","unstructured":"Fuglede, B., Topsoe, F.: Jensen-shannon divergence and hilbert space embedding. In: International Symposium on Information Theory, 2004. ISIT 2004. Proceedings, p.\u00a031. IEEE (2004)"},{"issue":"1","key":"2_CR7","doi-asserted-by":"publisher","first-page":"1093","DOI":"10.1038\/s41467-023-36736-1","volume":"14","author":"Z Gao","year":"2023","unstructured":"Gao, Z., et al.: Hierarchical graph learning for protein-protein interaction. Nat. Commun. 14(1), 1093 (2023)","journal-title":"Nat. Commun."},{"issue":"17","key":"2_CR8","doi-asserted-by":"publisher","first-page":"i802","DOI":"10.1093\/bioinformatics\/bty573","volume":"34","author":"S Hashemifar","year":"2018","unstructured":"Hashemifar, S., Neyshabur, B., Khan, A.A., Xu, J.: Predicting protein-protein interactions through sequence-based deep learning. Bioinformatics 34(17), i802\u2013i810 (2018)","journal-title":"Bioinformatics"},{"key":"2_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-019-3220-8","volume":"20","author":"M Heinzinger","year":"2019","unstructured":"Heinzinger, M., et al.: Modeling aspects of the language of life through transfer-learning protein sequences. BMC Bioinform. 20, 1\u201317 (2019)","journal-title":"BMC Bioinform."},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Hu, L., Wang, X., Huang, Y.A., Hu, P., You, Z.H.: A survey on computational models for predicting protein\u2013protein interactions. Brief. Bioinform. 22(5), bbab036 (2021)","DOI":"10.1093\/bib\/bbab036"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Jin, M., et al.: Prollm: protein chain-of-thoughts enhanced LLM for protein-protein interaction prediction. arXiv preprint arXiv:2405.06649 (2024)","DOI":"10.1101\/2024.04.18.590025"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Jumper, J., et\u00a0al.: Highly accurate protein structure prediction with alphafold. nature 596(7873), 583\u2013589 (2021)","DOI":"10.1038\/s41586-021-03819-2"},{"key":"2_CR13","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"2_CR14","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"issue":"1","key":"2_CR15","doi-asserted-by":"publisher","first-page":"1240","DOI":"10.1038\/s41467-019-09177-y","volume":"10","author":"IA Kov\u00e1cs","year":"2019","unstructured":"Kov\u00e1cs, I.A., et al.: Network-based prediction of protein interactions. Nat. Commun. 10(1), 1240 (2019)","journal-title":"Nat. Commun."},{"issue":"4","key":"2_CR16","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2020","unstructured":"Lee, J., et al.: Biobert: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4), 1234\u20131240 (2020)","journal-title":"Bioinformatics"},{"issue":"8","key":"2_CR17","doi-asserted-by":"publisher","first-page":"1923","DOI":"10.3390\/molecules23081923","volume":"23","author":"H Li","year":"2018","unstructured":"Li, H., Gong, X.J., Yu, H., Zhou, C.: Deep neural network based predictions of protein interactions using primary sequences. Molecules 23(8), 1923 (2018)","journal-title":"Molecules"},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Lin, J.S., Lai, E.M.: Protein\u2013protein interactions: co-immunoprecipitation. Bacterial protein secretion systems: Methods and protocols, pp. 211\u2013219 (2017)","DOI":"10.1007\/978-1-4939-7033-9_17"},{"key":"2_CR19","unstructured":"Liu, Y., et al.: Gut microbiota and tuberculosis. Imeta 4(4), e70054 (2025)"},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Lv, G., Hu, Z., Bi, Y., Zhang, S.: Learning unknown from correlations: Graph neural network for inter-novel-protein interaction prediction. arXiv preprint arXiv:2105.06709 (2021)","DOI":"10.24963\/ijcai.2021\/506"},{"issue":"8","key":"2_CR21","doi-asserted-by":"publisher","first-page":"1963","DOI":"10.3390\/molecules23081963","volume":"23","author":"SJY Macalino","year":"2018","unstructured":"Macalino, S.J.Y., Basith, S., Clavio, N.A.B., Chang, H., Kang, S., Choi, S.: Evolution of in silico strategies for protein-protein interaction drug discovery. Molecules 23(8), 1963 (2018)","journal-title":"Molecules"},{"issue":"14","key":"2_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmb.2023.168052","volume":"435","author":"D Petrey","year":"2023","unstructured":"Petrey, D., Zhao, H., Trudeau, S.J., Murray, D., Honig, B.: Preppi: a structure informed proteome-wide database of protein-protein interactions. J. Mol. Biol. 435(14), 168052 (2023)","journal-title":"J. Mol. Biol."},{"key":"2_CR23","unstructured":"Radford, Aet\u00a0al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Song, B., Luo, X., Luo, X., Liu, Y., Niu, Z., Zeng, X.: Learning spatial structures of proteins improves protein\u2013protein interaction prediction. Brief. Bioinform. 23(2), bbab558 (2022)","DOI":"10.1093\/bib\/bbab558"},{"key":"2_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-017-1700-2","volume":"18","author":"T Sun","year":"2017","unstructured":"Sun, T., Zhou, B., Lai, L., Pei, J.: Sequence-based prediction of protein protein interaction using a deep-learning algorithm. BMC Bioinform. 18, 1\u20138 (2017)","journal-title":"BMC Bioinform."},{"issue":"D1","key":"2_CR26","doi-asserted-by":"publisher","first-page":"D607","DOI":"10.1093\/nar\/gky1131","volume":"47","author":"D Szklarczyk","year":"2019","unstructured":"Szklarczyk, D., et al.: String v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47(D1), D607\u2013D613 (2019)","journal-title":"Nucleic Acids Res."},{"key":"2_CR27","doi-asserted-by":"crossref","unstructured":"Tang, T., et al.: Machine learning on protein\u2013protein interaction prediction: models, challenges and trends. Brief. Bioinform. 24(2), bbad076 (2023)","DOI":"10.1093\/bib\/bbad076"},{"issue":"1","key":"2_CR28","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1186\/s12915-025-02356-y","volume":"23","author":"F Wang","year":"2025","unstructured":"Wang, F., Chu, J., Shen, L., Chang, S.: Mesm: integrating multi-source data for high-accuracy protein-protein interactions prediction through multimodal language models. BMC Biol. 23(1), 253 (2025)","journal-title":"BMC Biol."},{"key":"2_CR29","doi-asserted-by":"crossref","unstructured":"Wang, Y., Min, Y., Chen, X., Wu, J.: Multi-view graph contrastive representation learning for drug-drug interaction prediction. In: Proceedings of the Web Conference 2021, pp. 2921\u20132933 (2021)","DOI":"10.1145\/3442381.3449786"},{"key":"2_CR30","unstructured":"Wu, L., et al.: Mape-ppi: Towards effective and efficient protein-protein interaction prediction via microenvironment-aware protein embedding. arXiv preprint arXiv:2402.14391 (2024)"},{"issue":"9","key":"2_CR31","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0238915","volume":"15","author":"Z Xiao","year":"2020","unstructured":"Xiao, Z., Deng, Y.: Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network. PLoS ONE 15(9), e0238915 (2020)","journal-title":"PLoS ONE"},{"key":"2_CR32","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018)"},{"issue":"1","key":"2_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.59717\/j.xinn-life.2026.100196","volume":"4","author":"G Yang","year":"2026","unstructured":"Yang, G., et al.: A comprehensive survey on artificial intelligence for biomolecule design. Innov. Life 4(1), 1\u201310019 (2026)","journal-title":"Innov. Life"},{"key":"2_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, C., Song, D., Huang, C., Swami, A., Chawla, N.V.: Heterogeneous graph neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 793\u2013803 (2019)","DOI":"10.1145\/3292500.3330961"},{"key":"2_CR35","doi-asserted-by":"crossref","unstructured":"Zhang, R., Wang, X., Wang, S., Liu, K., Zhou, Y., Wang, P.: H2d: hierarchical heterogeneous graph learning framework for drug-drug interaction prediction. In: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, pp. 4283\u20134287 (2024)","DOI":"10.1145\/3627673.3679936"},{"key":"2_CR36","doi-asserted-by":"crossref","unstructured":"Zhang, R., Wang, Z., Wang, X., Meng, Z., Cui, W.: MHTAN-DTI: Metapath-based hierarchical transformer and attention network for drug\u2013target interaction prediction. Brief. Bioinform. 24(2), bbad079 (2023)","DOI":"10.1093\/bib\/bbad079"},{"key":"2_CR37","unstructured":"Zhang, Z., et al.: Protein representation learning by geometric structure pretraining. arXiv preprint arXiv:2203.06125 (2022)"},{"key":"2_CR38","doi-asserted-by":"crossref","unstructured":"Zhao, Z., et al.: Semignn-ppi: Self-ensembling multi-graph neural network for efficient and generalizable protein-protein interaction prediction. arXiv preprint arXiv:2305.08316 (2023)","DOI":"10.24963\/ijcai.2023\/554"},{"key":"2_CR39","doi-asserted-by":"crossref","unstructured":"Zhuo, L., et al.: Protllm: An interleaved protein-language LLM with protein-as-word pre-training. arXiv preprint arXiv:2403.07920 (2024)","DOI":"10.18653\/v1\/2024.acl-long.484"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-0369-7_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T15:23:20Z","timestamp":1784215400000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-0369-7_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819203680","9789819203697"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-0369-7_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"12 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jeju","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 April 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 April 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dasfaa2026.github.io\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}