{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T20:48:25Z","timestamp":1743108505969,"version":"3.40.3"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031824838"},{"type":"electronic","value":"9783031824845"}],"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\/978-3-031-82484-5_11","type":"book-chapter","created":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T15:22:45Z","timestamp":1741015365000},"page":"145-159","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Compact Artificial Neural Network Models for\u00a0Predicting Protein Residue - RNA Base Binding"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1758-0171","authenticated-orcid":false,"given":"Stanislav","family":"Selitskiy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,4]]},"reference":[{"key":"11_CR1","doi-asserted-by":"publisher","DOI":"10.3389\/fmolb.2022.969394","volume":"9","author":"T Aderinwale","year":"2022","unstructured":"Aderinwale, T., Christoffer, C., Kihara, D.: Rl-mlzerd: multimeric protein docking using reinforcement learning. Front. Mol. Biosci. 9, 969394 (2022)","journal-title":"Front. Mol. Biosci."},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Asim, M.N., Ibrahim, M.A., Malik, M.I., Dengel, A., Ahmed, S.: Adh-ppi: an attention-based deep hybrid model for protein-protein interaction prediction. Iscience 25(10) (2022)","DOI":"10.1016\/j.isci.2022.105169"},{"key":"11_CR3","doi-asserted-by":"crossref","unstructured":"Carson, M.B., Langlois, R., Lu, H.: Naps: a residue-level nucleic acid-binding prediction server. Nucleic Acids Res. 38(suppl_2), W431\u2013W435 (2010)","DOI":"10.1093\/nar\/gkq361"},{"issue":"3","key":"11_CR4","doi-asserted-by":"publisher","first-page":"e15","DOI":"10.1093\/nar\/gkt1299","volume":"42","author":"YC Chen","year":"2014","unstructured":"Chen, Y.C., Sargsyan, K., Wright, J.D., Huang, Y.S., Lim, C.: Identifying rna-binding residues based on evolutionary conserved structural and energetic features. Nucleic Acids Res. 42(3), e15\u2013e15 (2014)","journal-title":"Nucleic Acids Res."},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233\u2013240 (2006)","DOI":"10.1145\/1143844.1143874"},{"issue":"4","key":"11_CR6","doi-asserted-by":"publisher","first-page":"747","DOI":"10.3892\/ijmm.2014.1629","volume":"33","author":"CM Di Liegro","year":"2014","unstructured":"Di Liegro, C.M., Schiera, G., Di Liegro, I.: Regulation of mrna transport, localization and translation in the nervous system of mammals (review). Int. J. Mol. Med. 33(4), 747\u2013762 (2014). https:\/\/doi.org\/10.3892\/ijmm.2014.1629","journal-title":"Int. J. Mol. Med."},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Dill, K.A., MacCallum, J.L.: The protein-folding problem, 50 years on. Science 338(6110), 1042\u20131046 (2012)","DOI":"10.1126\/science.1219021"},{"key":"11_CR8","unstructured":"Gasteiger, J., Gro\u00df, J., G\u00fcnnemann, S.: Directional message passing for molecular graphs. arXiv preprint arXiv:2003.03123 (2020)"},{"issue":"3","key":"11_CR9","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1039\/D2DD00008C","volume":"1","author":"M Haghighatlari","year":"2022","unstructured":"Haghighatlari, M., et al.: Newtonnet: a newtonian message passing network for deep learning of interatomic potentials and forces. Digital Discovery 1(3), 333\u2013343 (2022)","journal-title":"Digital Discovery"},{"issue":"1","key":"11_CR10","doi-asserted-by":"publisher","first-page":"1887","DOI":"10.1038\/srep01887","volume":"3","author":"Y Huang","year":"2013","unstructured":"Huang, Y., Liu, S., Guo, D., Li, L., Xiao, Y.: A novel protocol for three-dimensional structure prediction of rna-protein complexes. Sci. Rep. 3(1), 1887 (2013)","journal-title":"Sci. Rep."},{"issue":"1","key":"11_CR11","first-page":"105","volume":"15","author":"E Jeong","year":"2004","unstructured":"Jeong, E., Chung, I.F., Miyano, S.: A neural network method for identification of rna-interacting residues in protein. Genome Inform. 15(1), 105\u2013116 (2004)","journal-title":"Genome Inform."},{"key":"11_CR12","unstructured":"Joshi, C.K., Jamasb, A.R., Vi\u00f1as, R., Harris, C., Mathis, S., Li\u00f2, P.: Multi-state rna design with geometric multi-graph neural networks. arXiv preprint arXiv:2305.14749 (2023)"},{"key":"11_CR13","doi-asserted-by":"publisher","unstructured":"Jumper, J.e.a.: Highly accurate protein structure prediction with alphafold. Nature 596(7873), 583\u2013589 (2021). https:\/\/doi.org\/10.1038\/s41586-021-03819-2","DOI":"10.1038\/s41586-021-03819-2"},{"key":"11_CR14","doi-asserted-by":"publisher","unstructured":"Kashiwagi, S., Sato, K., Sakakibara, Y.: A max-margin model for predicting residue-base contacts in protein-rna interactions. Life (Basel) 11(11) (2021). https:\/\/doi.org\/10.3390\/life11111135","DOI":"10.3390\/life11111135"},{"issue":"22","key":"11_CR15","doi-asserted-by":"publisher","first-page":"6450","DOI":"10.1093\/nar\/gkl819","volume":"34","author":"OT Kim","year":"2006","unstructured":"Kim, O.T., Yura, K., Go, N.: Amino acid residue doublet propensity in the protein-rna interface and its application to rna interface prediction. Nucleic Acids Res. 34(22), 6450\u20136460 (2006)","journal-title":"Nucleic Acids Res."},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Kolmogorov, A.N.: On the representation of continuous functions of several variables by superpositions of continuous functions of a smaller number of variables. American Math. Soc. (1961)","DOI":"10.1090\/trans2\/017\/12"},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Kumar, M., Gromiha, M.M., Raghava, G.P.S.: Prediction of rna binding sites in a protein using svm and pssm profile. Proteins: Struct. Function Bioinform. 71(1), 189\u2013194 (2008)","DOI":"10.1002\/prot.21677"},{"key":"11_CR18","unstructured":"Le, T., Noe, F., Clevert, D.A.: Representation learning on biomolecular structures using equivariant graph attention. In: Learning on Graphs Conference, pp. 30\u20131. PMLR (2022)"},{"key":"11_CR19","doi-asserted-by":"publisher","unstructured":"Li, J., Liu, C.: Coding or noncoding, the converging concepts of rnas. Front. Genet. 10 (2019). https:\/\/doi.org\/10.3389\/fgene.2019.00496, https:\/\/www.frontiersin.org\/articles\/10.3389\/fgene.2019.00496","DOI":"10.3389\/fgene.2019.00496"},{"issue":"7221","key":"11_CR20","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1038\/nature07488","volume":"456","author":"DD Licatalosi","year":"2008","unstructured":"Licatalosi, D.D., et al.: Hits-clip yields genome-wide insights into brain alternative rna processing. Nature 456(7221), 464\u2013469 (2008)","journal-title":"Nature"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Ma, X., Guo, J., Wu, J., Liu, H., Yu, J., Xie, J., Sun, X.: Prediction of rna-binding residues in proteins from primary sequence using an enriched random forest model with a novel hybrid feature. Proteins: Struct. Function Bioinfor. 79(4), 1230\u20131239 (2011)","DOI":"10.1002\/prot.22958"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Murakami, Y., Spriggs, R.V., Nakamura, H., Jones, S.: Piranha: a server for the computational prediction of rna-binding residues in protein sequences. Nucleic Acids Res. 38(suppl_2), W412\u2013W416 (2010)","DOI":"10.1093\/nar\/gkq474"},{"issue":"1","key":"11_CR23","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1038\/s41557-022-01111-y","volume":"15","author":"MP Nikitin","year":"2023","unstructured":"Nikitin, M.P.: Non-complementary strand commutation as a fundamental alternative for information processing by DNA and gene regulation. Nat. Chem. 15(1), 70\u201382 (2023)","journal-title":"Nat. Chem."},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Pellegrini, F., Lot, R., Shaidu, Y., K\u00fc\u00e7\u00fckbenli, E.: Panna 2.0: efficient neural network interatomic potentials and new architectures. arXiv preprint arXiv:2305.11805 (2023)","DOI":"10.1063\/5.0158075"},{"key":"11_CR25","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-Cano, L., Fern\u00e1ndez-Recio, J.: Optimal protein-rna area, opra: a propensity-based method to identify rna-binding sites on proteins. Proteins: Struct. Function Bioinform. 78(1), 25\u201335 (2010)","DOI":"10.1002\/prot.22527"},{"issue":"3","key":"11_CR26","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.jsb.2011.10.001","volume":"179","author":"T Puton","year":"2012","unstructured":"Puton, T., Kozlowski, L., Tuszynska, I., Rother, K., Bujnicki, J.M.: Computational methods for prediction of protein-rna interactions. J. Struct. Biol. 179(3), 261\u2013268 (2012)","journal-title":"J. Struct. Biol."},{"key":"11_CR27","doi-asserted-by":"publisher","unstructured":"Sato, K.: The dataset used in \u201cA max-margin model for predicting residue-base contacts in protein-RNA interactions.\u201d Zenodo (2021). https:\/\/doi.org\/10.5281\/zenodo.5584470","DOI":"10.5281\/zenodo.5584470"},{"issue":"7268","key":"11_CR28","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1038\/nature08403","volume":"461","author":"TM Schmeing","year":"2009","unstructured":"Schmeing, T.M., Ramakrishnan, V.: What recent ribosome structures have revealed about the mechanism of translation. Nature 461(7268), 1234\u20131242 (2009)","journal-title":"Nature"},{"key":"11_CR29","doi-asserted-by":"crossref","unstructured":"Sch\u00fctt, K.T., Sauceda, H.E., Kindermans, P.J., Tkatchenko, A., M\u00fcller, K.R.: Schnet\u2013a deep learning architecture for molecules and materials. J. Chem. Phys. 148(24) (2018)","DOI":"10.1063\/1.5019779"},{"key":"11_CR30","doi-asserted-by":"crossref","unstructured":"Selitskiy, S.: Kolmogorov\u2019s gate non-linearity as a step toward much smaller artificial neural networks. In: Proceedings of the 24th International Conference on Enterprise Information Systems, vol.\u00a01, p. 492\u2013499 (2022)","DOI":"10.5220\/0011060700003179"},{"key":"11_CR31","doi-asserted-by":"publisher","unstructured":"Selitskiy, S.: Explicit model memorisation to fight forgetting in time-series prediction. In: SoutheastCon 2024, pp. 660\u2013667 (2024). https:\/\/doi.org\/10.1109\/SoutheastCon52093.2024.10500223","DOI":"10.1109\/SoutheastCon52093.2024.10500223"},{"key":"11_CR32","doi-asserted-by":"publisher","unstructured":"Selitskiy, S.: Weak relation enforcement for kinematic-informed long-term stock prediction with artificial neural networks. In: Arai, K. (ed.) Intelligent Computing, pp. 249\u2013261. Springer Nature Switzerland, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-62269-4_18","DOI":"10.1007\/978-3-031-62269-4_18"},{"key":"11_CR33","doi-asserted-by":"publisher","unstructured":"Selitskiy, S., Inoue, C., Schetinin, V., Jakaite, L.: The batch primary components transformer and auto-plasticity learning linear units architecture: Synthetic image generation case. In: 2023 Tenth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp.\u00a01\u20139 (2023). https:\/\/doi.org\/10.1109\/SNAMS60348.2023.10375471","DOI":"10.1109\/SNAMS60348.2023.10375471"},{"issue":"4","key":"11_CR34","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1016\/j.cels.2020.08.016","volume":"11","author":"A Strokach","year":"2020","unstructured":"Strokach, A., Becerra, D., Corbi-Verge, C., Perez-Riba, A., Kim, P.M.: Fast and flexible protein design using deep graph neural networks. Cell Syst. 11(4), 402\u2013411 (2020)","journal-title":"Cell Syst."},{"key":"11_CR35","doi-asserted-by":"crossref","unstructured":"Terribilini, M., et al.: Rnabindr: a server for analyzing and predicting rna-binding sites in proteins. Nucleic Acids Res. 35(suppl_2), W578\u2013W584 (2007)","DOI":"10.1093\/nar\/gkm294"},{"key":"11_CR36","doi-asserted-by":"crossref","unstructured":"Thompson, M.C., Yeates, T.O., Rodriguez, J.A.: Advances in methods for atomic resolution macromolecular structure determination. F1000 Res. 9 (2020)","DOI":"10.12688\/f1000research.25097.1"},{"key":"11_CR37","doi-asserted-by":"crossref","unstructured":"Tong, J., Jiang, P., Lu, Z.h.: Risp: a web-based server for prediction of rna-binding sites in proteins. Comput. Methods Programs Biomed. 90(2), 148\u2013153 (2008)","DOI":"10.1016\/j.cmpb.2007.12.003"},{"key":"11_CR38","doi-asserted-by":"crossref","unstructured":"Wang, L., Brown, S.J.: Bindn: a web-based tool for efficient prediction of dna and rna binding sites in amino acid sequences. Nucleic Acids Res. 34(suppl_2), W243\u2013W248 (2006)","DOI":"10.1093\/nar\/gkl298"},{"key":"11_CR39","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/s00726-007-0634-9","volume":"35","author":"Y Wang","year":"2008","unstructured":"Wang, Y., Xue, Z., Shen, G., Xu, J.: Printr: prediction of rna binding sites in proteins using svm and profiles. Amino Acids 35, 295\u2013302 (2008)","journal-title":"Amino Acids"},{"issue":"1","key":"11_CR40","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1038\/s42003-020-1114-y","volume":"3","author":"J Xie","year":"2020","unstructured":"Xie, J., Zheng, J., Hong, X., Tong, X., Liu, S.: Prime-3d2d is a 3d2d model to predict binding sites of protein-rna interaction. Commun. Biol. 3(1), 384 (2020). https:\/\/doi.org\/10.1038\/s42003-020-1114-y","journal-title":"Commun. Biol."},{"key":"11_CR41","unstructured":"Zhang, J., et\u00a0al.: Few-shot learning of accurate folding landscape for protein structure prediction. arXiv preprint arXiv:2208.09652 (2022)"},{"issue":"8","key":"11_CR42","doi-asserted-by":"publisher","first-page":"3017","DOI":"10.1093\/nar\/gkq1266","volume":"39","author":"H Zhao","year":"2011","unstructured":"Zhao, H., Yang, Y., Zhou, Y.: Structure-based prediction of rna-binding domains and rna-binding sites and application to structural genomics targets. Nucleic Acids Res. 39(8), 3017\u20133025 (2011)","journal-title":"Nucleic Acids Res."},{"issue":"9","key":"11_CR43","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1005120","volume":"12","author":"J Zheng","year":"2016","unstructured":"Zheng, J., Kundrotas, P.J., Vakser, I.A., Liu, S.: Template-based modeling of protein-rna interactions. PLoS Comput. Biol. 12(9), e1005120 (2016)","journal-title":"PLoS Comput. Biol."}],"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\/978-3-031-82484-5_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,3]],"date-time":"2025-03-03T15:22:56Z","timestamp":1741015376000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-82484-5_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031824838","9783031824845"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-82484-5_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"4 March 2025","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":"Castiglione della Pescaia","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mod2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2024.icas.events\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}