{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T05:22:11Z","timestamp":1771478531436,"version":"3.50.1"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031878725","type":"print"},{"value":"9783031878732","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\/978-3-031-87873-2_4","type":"book-chapter","created":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T09:11:03Z","timestamp":1745485863000},"page":"31-40","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["PPI Prediction from Sequences via Transfer Learning on Balanced but yet Biased Datasets: An Open Problem"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5991-7698","authenticated-orcid":false,"given":"Alba","family":"Nogueira-Rodr\u00edguez","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6129-7245","authenticated-orcid":false,"given":"Daniel","family":"Glez-Pe\u00f1a","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7139-2107","authenticated-orcid":false,"given":"Cristina P.","family":"Vieira","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7032-5220","authenticated-orcid":false,"given":"Jorge","family":"Vieira","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6476-7206","authenticated-orcid":false,"given":"Hugo","family":"L\u00f3pez-Fern\u00e1ndez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,25]]},"reference":[{"key":"4_CR1","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1093\/bfgp\/els036","volume":"11","author":"J De Las Rivas","year":"2012","unstructured":"De Las Rivas, J., Fontanillo, C.: Protein-protein interaction networks: unraveling the wiring of molecular machines within the cell. Brief. Funct. Genomics 11, 489\u2013496 (2012). https:\/\/doi.org\/10.1093\/bfgp\/els036","journal-title":"Brief. Funct. Genomics"},{"key":"4_CR2","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1038\/nrg1272","volume":"5","author":"A-L Barab\u00e1si","year":"2004","unstructured":"Barab\u00e1si, A.-L., Oltvai, Z.N.: Network biology: understanding the cell\u2019s functional organization. Nat. Rev. Genet. 5, 101\u2013113 (2004). https:\/\/doi.org\/10.1038\/nrg1272","journal-title":"Nat. Rev. Genet."},{"key":"4_CR3","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1101\/gr.071852.107","volume":"18","author":"T Ideker","year":"2008","unstructured":"Ideker, T., Sharan, R.: Protein networks in disease. Genome Res. 18, 644\u2013652 (2008). https:\/\/doi.org\/10.1101\/gr.071852.107","journal-title":"Genome Res."},{"key":"4_CR4","doi-asserted-by":"publisher","first-page":"986","DOI":"10.1016\/j.cell.2011.02.016","volume":"144","author":"M Vidal","year":"2011","unstructured":"Vidal, M., Cusick, M.E., Barab\u00e1si, A.-L.: Interactome networks and human disease. Cell 144, 986\u2013998 (2011). https:\/\/doi.org\/10.1016\/j.cell.2011.02.016","journal-title":"Cell"},{"key":"4_CR5","doi-asserted-by":"publisher","first-page":"102775","DOI":"10.1016\/j.sbi.2024.102775","volume":"85","author":"J Zhang","year":"2024","unstructured":"Zhang, J., Durham, J.: Qian cong: revolutionizing protein\u2013protein interaction prediction with deep learning. Curr. Opin. Struct. Biol. 85, 102775 (2024). https:\/\/doi.org\/10.1016\/j.sbi.2024.102775","journal-title":"Curr. Opin. Struct. Biol."},{"key":"4_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12033-007-0069-2","volume":"38","author":"L Skrabanek","year":"2008","unstructured":"Skrabanek, L., Saini, H.K., Bader, G.D., Enright, A.J.: Computational prediction of protein-protein interactions. Mol. Biotechnol. 38, 1\u201317 (2008). https:\/\/doi.org\/10.1007\/s12033-007-0069-2","journal-title":"Mol. Biotechnol."},{"key":"4_CR7","doi-asserted-by":"publisher","unstructured":"Bernett, J., Blumenthal, D.B., List, M.: Cracking the black box of deep sequence-based protein\u2013protein interaction prediction. Brief. Bioinform. 25, bbae076 (2024). https:\/\/doi.org\/10.1093\/bib\/bbae076","DOI":"10.1093\/bib\/bbae076"},{"key":"4_CR8","doi-asserted-by":"publisher","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient Estimation of Word Representations in Vector Space (2013). https:\/\/doi.org\/10.48550\/ARXIV.1301.3781","DOI":"10.48550\/ARXIV.1301.3781"},{"key":"4_CR9","doi-asserted-by":"publisher","first-page":"723","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, 723 (2019). https:\/\/doi.org\/10.1186\/s12859-019-3220-8","journal-title":"BMC Bioinform."},{"key":"4_CR10","doi-asserted-by":"publisher","first-page":"7112","DOI":"10.1109\/TPAMI.2021.3095381","volume":"44","author":"A Elnaggar","year":"2022","unstructured":"Elnaggar, A., et al.: ProtTrans: toward understanding the language of life through self-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 44, 7112\u20137127 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2021.3095381","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"4_CR11","doi-asserted-by":"publisher","first-page":"3025","DOI":"10.1093\/nar\/gkn159","volume":"36","author":"Y Guo","year":"2008","unstructured":"Guo, Y., Yu, L., Wen, Z., Li, M.: Using support vector machine combined with auto covariance to predict protein\u2013protein interactions from protein sequences. Nucleic Acids Res. 36, 3025\u20133030 (2008). https:\/\/doi.org\/10.1093\/nar\/gkn159","journal-title":"Nucleic Acids Res."},{"key":"4_CR12","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.1038\/nmeth.2259","volume":"9","author":"Y Park","year":"2012","unstructured":"Park, Y., Marcotte, E.M.: Flaws in evaluation schemes for pair-input computational predictions. Nat. Methods 9, 1134\u20131136 (2012). https:\/\/doi.org\/10.1038\/nmeth.2259","journal-title":"Nat. Methods"},{"key":"4_CR13","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.artmed.2017.03.001","volume":"83","author":"L Wei","year":"2017","unstructured":"Wei, L., Xing, P., Zeng, J., Chen, J., Su, R., Guo, F.: Improved prediction of protein\u2013protein interactions using novel negative samples, features, and an ensemble classifier. Artif. Intell. Med. 83, 67\u201374 (2017). https:\/\/doi.org\/10.1016\/j.artmed.2017.03.001","journal-title":"Artif. Intell. Med."},{"key":"4_CR14","doi-asserted-by":"publisher","unstructured":"Nogueira-Rodriguez, A., Glez-Pe\u00f1a, D.P. Vieira, C., Vieira, J., L\u00f3pez-Fern\u00e1ndez, H.: PPI prediction from sequences via transfer learning on balanced but yet biased datasets: an open problem (2024). https:\/\/doi.org\/10.5281\/zenodo.10903300","DOI":"10.5281\/zenodo.10903300"},{"key":"4_CR15","doi-asserted-by":"publisher","unstructured":"Blohm, P., et al.: Negatome 2.0: a database of non-interacting proteins derived by literature mining, manual annotation and protein structure analysis. Nucl. Acids Res. 42, D396\u2013D400 (2014). https:\/\/doi.org\/10.1093\/nar\/gkt1079","DOI":"10.1093\/nar\/gkt1079"},{"key":"4_CR16","doi-asserted-by":"publisher","first-page":"e113","DOI":"10.1002\/cpz1.113","volume":"1","author":"C Dallago","year":"2021","unstructured":"Dallago, C., et al.: Learned embeddings from deep learning to visualize and predict protein sets. Curr. Protoc. 1, e113 (2021). https:\/\/doi.org\/10.1002\/cpz1.113","journal-title":"Curr. Protoc."}],"container-title":["Lecture Notes in Networks and Systems","Practical Applications of Computational Biology and Bioinformatics, 18th International Conference (PACBB 2024)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-87873-2_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T09:11:03Z","timestamp":1745485863000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-87873-2_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031878725","9783031878732"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-87873-2_4","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"value":"2367-3370","type":"print"},{"value":"2367-3389","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"25 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"All authors have declared no conflict of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"PACBB","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Practical Applications of Computational Biology & Bioinformatics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Salamanca","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 June 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pacbb2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pacbb.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}