{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T04:03:28Z","timestamp":1745467408297,"version":"3.40.4"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031900648","type":"print"},{"value":"9783031900655","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\/978-3-031-90065-5_30","type":"book-chapter","created":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T03:08:22Z","timestamp":1745377702000},"page":"492-506","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Symbolic Regression Screening Approach Within Peptide Optimisation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6209-4642","authenticated-orcid":false,"given":"Aidan","family":"Murphy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7313-2325","authenticated-orcid":false,"given":"Mark","family":"Kocherovsky","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3695-0986","authenticated-orcid":false,"given":"Nir","family":"Dayan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2247-5253","authenticated-orcid":false,"given":"Ilya","family":"Miralavy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7272-7570","authenticated-orcid":false,"given":"Assaf","family":"Gilad","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6382-3245","authenticated-orcid":false,"given":"Wolfgang","family":"Banzhaf","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,17]]},"reference":[{"issue":"2","key":"30_CR1","doi-asserted-by":"publisher","first-page":"430","DOI":"10.3390\/molecules26020430","volume":"26","author":"V Apostolopoulos","year":"2021","unstructured":"Apostolopoulos, V., et al.: A global review on short peptides: Frontiers and perspectives. Molecules 26(2), 430 (2021)","journal-title":"Molecules"},{"issue":"4","key":"30_CR2","doi-asserted-by":"publisher","first-page":"678","DOI":"10.1002\/pro.3588","volume":"28","author":"D Baker","year":"2019","unstructured":"Baker, D.: What has de novo protein design taught us about protein folding and biophysics? Protein Sci. 28(4), 678\u2013683 (2019)","journal-title":"Protein Sci."},{"key":"30_CR3","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, 5\u201332 (2001)","journal-title":"Random forests. Mach. Learn."},{"key":"30_CR4","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XgBoost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794 (2016)","DOI":"10.1145\/2939672.2939785"},{"issue":"6","key":"30_CR5","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1038\/s42256-022-00499-z","volume":"4","author":"N Ferruz","year":"2022","unstructured":"Ferruz, N., H\u00f6cker, B.: Controllable protein design with language models. Nature Machine Intelligence 4(6), 521\u2013532 (2022)","journal-title":"Nature Machine Intelligence"},{"issue":"6","key":"30_CR6","doi-asserted-by":"publisher","DOI":"10.1002\/nbm.4712","volume":"36","author":"AA Gilad","year":"2023","unstructured":"Gilad, A.A., Bar-Shir, A., Bricco, A.R., Mohanta, Z., McMahon, M.T.: Protein and peptide engineering for chemical exchange saturation transfer imaging in the age of synthetic biology. NMR Biomed. 36(6), e4712 (2023)","journal-title":"NMR Biomed."},{"key":"30_CR7","doi-asserted-by":"crossref","unstructured":"Haut, N., Banzhaf, W., Punch, B.: Active learning in Genetic Programming: Guiding efficient data collection for symbolic regression. IEEE Trans. Evolutionary Comput. (2024)","DOI":"10.1109\/TEVC.2024.3471341"},{"key":"30_CR8","doi-asserted-by":"crossref","unstructured":"Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., \u017d\u00eddek, A., Potapenko, A., et\u00a0al.: Highly accurate protein structure prediction with Alphafold. Nature 596(7873), 583\u2013589 (2021)","DOI":"10.1038\/s41586-021-03819-2"},{"key":"30_CR9","unstructured":"Ke, G., et al.: Lightgbm: a highly efficient gradient boosting decision tree. Advances in neural information processing systems 30 (2017)"},{"key":"30_CR10","doi-asserted-by":"crossref","unstructured":"Knuth, D.E.: Backus normal form vs. backus naur form. Communications of the ACM 7(12), 735\u2013736 (1964)","DOI":"10.1145\/355588.365140"},{"key":"30_CR11","doi-asserted-by":"crossref","unstructured":"Li, K., et\u00a0al.: Explainable machine learning identifies multi-omics signatures of muscle response to spaceflight in mice. npj Microgravity 9(1), 90 (2023)","DOI":"10.1038\/s41526-023-00337-5"},{"key":"30_CR12","doi-asserted-by":"crossref","unstructured":"Louren\u00e7o, N., Pereira, F.B., Costa, E.: SGE: A structured representation for Grammatical Evolution. In: International Conference on Artificial Evolution (Evolution Artificielle), pp. 136\u2013148. Springer (2015)","DOI":"10.1007\/978-3-319-31471-6_11"},{"key":"30_CR13","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-pchem.24","volume":"4","author":"I Miralavy","year":"2022","unstructured":"Miralavy, I., Bricco, A.R., Gilad, A.A., Banzhaf, W.: Using genetic programming to predict and optimize protein function. PeerJ Physical Chemistry 4, e24 (2022)","journal-title":"PeerJ Physical Chemistry"},{"key":"30_CR14","doi-asserted-by":"publisher","unstructured":"Murphy, A., Mahdinejad, M., Ventresque, A., Louren\u00e7o, N.: An investigation into structured grammatical evolution initialisation. Genet. Program Evolvable Mach. 25(2), 24 (2024). https:\/\/doi.org\/10.1007\/s10710-024-09498-y","DOI":"10.1007\/s10710-024-09498-y"},{"issue":"2","key":"30_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-022-01044-w","volume":"3","author":"A Murphy","year":"2022","unstructured":"Murphy, A., Murphy, G., Dias, D.M., Amaral, J., Naredo, E., Ryan, C.: Human in the loop fuzzy pattern tree evolution. SN Computer Science 3(2), 1\u201314 (2022)","journal-title":"SN Computer Science"},{"issue":"1","key":"30_CR16","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/s10710-020-09391-4","volume":"22","author":"M Nicolau","year":"2021","unstructured":"Nicolau, M., Agapitos, A.: Choosing function sets with better generalisation performance for symbolic regression models. Genet. Program Evolvable Mach. 22(1), 73\u2013100 (2021)","journal-title":"Genet. Program Evolvable Mach."},{"issue":"11","key":"30_CR17","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1016\/j.cels.2023.10.002","volume":"14","author":"E Nijkamp","year":"2023","unstructured":"Nijkamp, E., Ruffolo, J.A., Weinstein, E.N., Naik, N., Madani, A.: ProGen2: Exploring the boundaries of protein language models. Cell Syst. 14(11), 968\u2013978 (2023)","journal-title":"Cell Syst."},{"key":"30_CR18","first-page":"44","volume":"12","author":"D Osorio","year":"2015","unstructured":"Osorio, D., Rond\u00f3n-Villarreal, P., Torres, R.: Peptides: a package for data mining of antimicrobial peptides. Small 12, 44\u2013444 (2015)","journal-title":"Small"},{"key":"30_CR19","doi-asserted-by":"publisher","unstructured":"Ryan, C., Collins, J.J., Neill, M.O.: Grammatical Evolution: Evolving programs for an arbitrary language. In: Banzhaf, W., Poli, R., Schoenauer, M., Fogarty, T.C. (eds.) EuroGP 1998. LNCS, vol. 1391, pp. 83\u201396. Springer, Heidelberg (1998). https:\/\/doi.org\/10.1007\/BFb0055930","DOI":"10.1007\/BFb0055930"},{"issue":"1","key":"30_CR20","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/s10822-024-00558-0","volume":"38","author":"N Scalzitti","year":"2024","unstructured":"Scalzitti, N., Miralavy, I., Korenchan, D.E., Farrar, C.T., Gilad, A.A., Banzhaf, W.: Computational peptide discovery with a Genetic Programming approach. J. Comput. Aided Mol. Des. 38(1), 17 (2024)","journal-title":"J. Comput. Aided Mol. Des."},{"issue":"1","key":"30_CR21","doi-asserted-by":"publisher","first-page":"7407","DOI":"10.1038\/s41467-024-51844-2","volume":"15","author":"R Schmirler","year":"2024","unstructured":"Schmirler, R., Heinzinger, M., Rost, B.: Fine-tuning protein language models boosts predictions across diverse tasks. Nat. Commun. 15(1), 7407 (2024)","journal-title":"Nat. Commun."}],"container-title":["Lecture Notes in Computer Science","Applications of Evolutionary Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-90065-5_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,23]],"date-time":"2025-04-23T03:08:29Z","timestamp":1745377709000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-90065-5_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031900648","9783031900655"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-90065-5_30","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":"17 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EvoApplications","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on the Applications of Evolutionary Computation (Part of EvoStar)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Trieste","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"23 April 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 April 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"evoapplications2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.evostar.org\/2025\/evoapps\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}