{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T20:58:41Z","timestamp":1764277121303,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030954666"},{"type":"electronic","value":"9783030954673"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-030-95467-3_41","type":"book-chapter","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T10:07:13Z","timestamp":1643710033000},"page":"568-580","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["ProSPs: Protein Sites Prediction Based on Sequence Fragments"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0539-0290","authenticated-orcid":false,"given":"Michela","family":"Quadrini","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Massimo","family":"Cavallin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0544-1465","authenticated-orcid":false,"given":"Sebastian","family":"Daberdaku","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3665-0446","authenticated-orcid":false,"given":"Carlo","family":"Ferrari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,2,2]]},"reference":[{"issue":"12","key":"41_CR1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0029104","volume":"6","author":"S Ahmad","year":"2011","unstructured":"Ahmad, S., Mizuguchi, K.: Partner-aware prediction of interacting residues in protein-protein complexes from sequence data. PLoS ONE 6(12), e29104 (2011)","journal-title":"PLoS ONE"},{"issue":"7","key":"41_CR2","doi-asserted-by":"publisher","first-page":"1545","DOI":"10.1162\/neco.1997.9.7.1545","volume":"9","author":"Y Amit","year":"1997","unstructured":"Amit, Y., Geman, D.: Shape quantization and recognition with randomized trees. Neural Comput. 9(7), 1545\u20131588 (1997)","journal-title":"Neural Comput."},{"issue":"16","key":"41_CR3","doi-asserted-by":"publisher","first-page":"2833","DOI":"10.1002\/pmic.200700131","volume":"7","author":"T Bergg\u00e5rd","year":"2007","unstructured":"Bergg\u00e5rd, T., Linse, S., James, P.: Methods for the detection and analysis of protein-protein interactions. Proteomics 7(16), 2833\u20132842 (2007)","journal-title":"Proteomics"},{"issue":"1","key":"41_CR4","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"41_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/978-3-030-34585-3_4","volume-title":"Computational Intelligence Methods for Bioinformatics and Biostatistics","author":"S Daberdaku","year":"2020","unstructured":"Daberdaku, S.: Structure-based antibody paratope prediction with 3D zernike descriptors and SVM. In: Raposo, M., Ribeiro, P., S\u00e9rio, S., Staiano, A., Ciaramella, A. (eds.) CIBB 2018. LNCS, vol. 11925, pp. 27\u201349. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-34585-3_4"},{"issue":"1","key":"41_CR6","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1186\/s12859-018-2043-3","volume":"19","author":"S Daberdaku","year":"2018","unstructured":"Daberdaku, S., Ferrari, C.: Exploring the potential of 3D Zernike descriptors and SVM for protein-protein interface prediction. BMC Bioinform. 19(1), 35 (2018)","journal-title":"BMC Bioinform."},{"issue":"11","key":"41_CR7","doi-asserted-by":"publisher","first-page":"1870","DOI":"10.1093\/bioinformatics\/bty918","volume":"35","author":"S Daberdaku","year":"2019","unstructured":"Daberdaku, S., Ferrari, C.: Antibody interface prediction with 3D Zernike descriptors and SVM. Bioinformatics 35(11), 1870\u20131876 (2019)","journal-title":"Bioinformatics"},{"issue":"6","key":"41_CR8","first-page":"535","volume":"84","author":"DC Fry","year":"2006","unstructured":"Fry, D.C.: Protein-protein interactions as targets for small molecule drug discovery. Peptide Sci. Original Res. Biomolecules 84(6), 535\u2013552 (2006)","journal-title":"Peptide Sci. Original Res. Biomolecules"},{"key":"41_CR9","doi-asserted-by":"crossref","unstructured":"Ho, T.K.: Random decision forests. In: Proceedings of 3rd International Conference on Document Analysis and Recognition, vol. 1, pp. 278\u2013282. IEEE (1995)","DOI":"10.1109\/ICDAR.1995.598994"},{"issue":"8","key":"41_CR10","doi-asserted-by":"publisher","first-page":"832","DOI":"10.1109\/34.709601","volume":"20","author":"TK Ho","year":"1998","unstructured":"Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832\u2013844 (1998)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"41_CR11","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1186\/1471-2105-13-41","volume":"13","author":"RA Jordan","year":"2012","unstructured":"Jordan, R.A., Yasser, E.M., Dobbs, D., Honavar, V.: Predicting protein-protein interface residues using local surface structural similarity. BMC Bioinform. 13(1), 41 (2012)","journal-title":"BMC Bioinform."},{"key":"41_CR12","doi-asserted-by":"crossref","unstructured":"Kawashima, S., Pokarowski, P., Pokarowska, M., Kolinski, A., Katayama, T., Kanehisa, M.: Aaindex: amino acid index database, progress report 2008. Nucleic Acids Res. 36(suppl$$_1$$), D202\u2013D205 (2007)","DOI":"10.1093\/nar\/gkm998"},{"issue":"8","key":"41_CR13","doi-asserted-by":"publisher","first-page":"4884","DOI":"10.1021\/acs.chemrev.5b00683","volume":"116","author":"O Keskin","year":"2016","unstructured":"Keskin, O., Tuncbag, N., Gursoy, A.: Predicting protein-protein interactions from the molecular to the proteome level. Chem. Rev. 116(8), 4884\u20134909 (2016)","journal-title":"Chem. Rev."},{"issue":"15","key":"41_CR14","doi-asserted-by":"publisher","first-page":"1841","DOI":"10.1093\/bioinformatics\/btq302","volume":"26","author":"Y Murakami","year":"2010","unstructured":"Murakami, Y., Mizuguchi, K.: Applying the na\u00efve bayes classifier with kernel density estimation to the prediction of protein-protein interaction sites. Bioinformatics 26(15), 1841\u20131848 (2010)","journal-title":"Bioinformatics"},{"key":"41_CR15","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011). http:\/\/jmlr.org\/papers\/v12\/pedregosa11a.html"},{"key":"41_CR16","doi-asserted-by":"crossref","unstructured":"Porollo, A., Meller, J.: Prediction-based fingerprints of protein-protein interactions. Proteins: Struct. Funct. Bioinform. 66(3), 630\u2013645 (2007)","DOI":"10.1002\/prot.21248"},{"key":"41_CR17","first-page":"3","volume":"472","author":"A Porollo","year":"2012","unstructured":"Porollo, A., Meller, J., Cai, W., Hong, H.: Computational methods for prediction of protein-protein interaction sites. Protein-Protein Interact. Comput. Exp. Tools 472, 3\u201326 (2012)","journal-title":"Protein-Protein Interact. Comput. Exp. Tools"},{"key":"41_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/978-3-319-71069-3_16","volume-title":"Theory and Practice of Natural Computing","author":"M Quadrini","year":"2017","unstructured":"Quadrini, M., Culmone, R., Merelli, E.: Topological classification of RNA structures via\u00a0intersection graph. In: Mart\u00edn-Vide, C., Neruda, R., Vega-Rodr\u00edguez, M.A. (eds.) TPNC 2017. LNCS, vol. 10687, pp. 203\u2013215. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-71069-3_16"},{"key":"41_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1007\/978-3-030-64580-9_34","volume-title":"Machine Learning, Optimization, and Data Science","author":"M Quadrini","year":"2020","unstructured":"Quadrini, M., Daberdaku, S., Ferrari, C.: Hierarchical representation and graph convolutional networks for the prediction of protein\u2013protein interaction sites. In: Nicosia, G., Ojha, V., La Malfa, E., Jansen, G., Sciacca, V., Pardalos, P., Giuffrida, G., Umeton, R. (eds.) LOD 2020. LNCS, vol. 12566, pp. 409\u2013420. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-64580-9_34"},{"key":"41_CR20","doi-asserted-by":"crossref","unstructured":"Quadrini., M., Merelli., E., Piergallini., R.: Loop grammars to identify RNA structural patterns. In: Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, pp. 302\u2013309. SciTePress (2019)","DOI":"10.5220\/0007576603020309"},{"key":"41_CR21","doi-asserted-by":"crossref","unstructured":"Quadrini, M., Tesei, L., Merelli, E.: ASPRAlign: a tool for the alignment of RNA secondary structures with arbitrary pseudoknots. Bioinformatics 36(11), 3578\u20133579 (2020)","DOI":"10.1093\/bioinformatics\/btaa147"},{"issue":"2","key":"41_CR22","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1007\/s00726-011-1106-9","volume":"43","author":"I Saha","year":"2012","unstructured":"Saha, I., Maulik, U., Bandyopadhyay, S., Plewczynski, D.: Fuzzy clustering of physicochemical and biochemical properties of amino acids. Amino Acids 43(2), 583\u2013594 (2012)","journal-title":"Amino Acids"},{"issue":"1","key":"41_CR23","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1000278","volume":"5","author":"M \u0160iki\u0107","year":"2009","unstructured":"\u0160iki\u0107, M., Tomi\u0107, S., Vlahovi\u010dek, K.: Prediction of protein-protein interaction sites in sequences and 3d structures by random forests. PLoS Comput. Biol. 5(1), e1000278 (2009)","journal-title":"PLoS Comput. Biol."},{"issue":"6","key":"41_CR24","doi-asserted-by":"publisher","first-page":"1394","DOI":"10.1109\/TCBB.2015.2401018","volume":"12","author":"BK Sriwastava","year":"2015","unstructured":"Sriwastava, B.K., Basu, S., Maulik, U.: Predicting protein-protein interaction sites with a novel membership based fuzzy SVM classifier. IEEE\/ACM Trans. Comput. Biol. Bioinf. 12(6), 1394\u20131404 (2015)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"issue":"19","key":"41_CR25","doi-asserted-by":"publisher","first-page":"3031","DOI":"10.1016\/j.jmb.2015.07.016","volume":"427","author":"T Vreven","year":"2015","unstructured":"Vreven, T., et al.: Updates to the integrated protein-protein interaction benchmarks: docking benchmark version 5 and affinity benchmark version 2. J. Mol. Biol. 427(19), 3031\u20133041 (2015)","journal-title":"J. Mol. Biol."},{"issue":"1","key":"41_CR26","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1186\/1471-2105-12-244","volume":"12","author":"LC Xue","year":"2011","unstructured":"Xue, L.C., Dobbs, D., Honavar, V.: Homppi: a class of sequence homology based protein-protein interface prediction methods. BMC Bioinform. 12(1), 244 (2011)","journal-title":"BMC Bioinform."},{"issue":"39","key":"41_CR27","doi-asserted-by":"publisher","first-page":"16622","DOI":"10.1073\/pnas.0906146106","volume":"106","author":"S Yin","year":"2009","unstructured":"Yin, S., Proctor, E.A., Lugovskoy, A.A., Dokholyan, N.V.: Fast screening of protein surfaces using geometric invariant fingerprints. Proc. Natl. Acad. Sci. 106(39), 16622\u201316626 (2009)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"41_CR28","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.neucom.2019.05.013","volume":"357","author":"B Zhang","year":"2019","unstructured":"Zhang, B., Li, J., Quan, L., Chen, Y., L\u00fc, Q.: Sequence-based prediction of protein-protein interaction sites by simplified long short-term memory network. Neurocomputing 357, 86\u2013100 (2019)","journal-title":"Neurocomputing"}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-95467-3_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,17]],"date-time":"2024-09-17T18:17:18Z","timestamp":1726597038000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-95467-3_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030954666","9783030954673"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-95467-3_41","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"2 February 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Optimization, and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grasmere","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mod2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2021.icas.cc\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"215","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"86","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5-6","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1-2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}