{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T15:01:47Z","timestamp":1742914907516,"version":"3.40.3"},"publisher-location":"Cham","reference-count":49,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9781071639887"},{"type":"electronic","value":"9781071639894"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-1-0716-3989-4_18","type":"book-chapter","created":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T08:02:38Z","timestamp":1715846558000},"page":"288-307","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Structure- and Function-Aware Substitution Matrices via\u00a0Learnable Graph Matching"],"prefix":"10.1007","author":[{"given":"Paolo","family":"Pellizzoni","sequence":"first","affiliation":[]},{"given":"Carlos","family":"Oliver","sequence":"additional","affiliation":[]},{"given":"Karsten","family":"Borgwardt","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,17]]},"reference":[{"key":"18_CR1","doi-asserted-by":"crossref","unstructured":"Bai, Y., Ding, H., Bian, S., Chen, T., Sun, Y., Wang, W.: SimGNN: a neural network approach to fast graph similarity computation. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 384\u2013392 (2019)","DOI":"10.1145\/3289600.3290967"},{"key":"18_CR2","doi-asserted-by":"crossref","unstructured":"Bateman, A., et al.: The PFAM protein families database. Nucleic Acids Res. 32(suppl_1), D138\u2013D141 (2004)","DOI":"10.1093\/nar\/gkh121"},{"issue":"14","key":"18_CR3","doi-asserted-by":"publisher","first-page":"5061","DOI":"10.1021\/jm100112j","volume":"53","author":"C Bissantz","year":"2010","unstructured":"Bissantz, C., Kuhn, B., Stahl, M.: A medicinal chemist\u2019s guide to molecular interactions. J. Med. Chem. 53(14), 5061\u20135084 (2010)","journal-title":"J. Med. Chem."},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Blumenthal, D.B., Boria, N., Gamper, J., Bougleux, S., Brun, L.: Comparing heuristics for graph edit distance computation. VLDB J. 29(1), 419\u2013458 (2020)","DOI":"10.1007\/s00778-019-00544-1"},{"key":"18_CR5","doi-asserted-by":"crossref","unstructured":", D.B., Gamper, J.: Improved lower bounds for graph edit distance. IEEE Trans. Knowl. Data Eng. 30(3), 503\u2013516 (2017)","DOI":"10.1109\/TKDE.2017.2772243"},{"key":"18_CR6","doi-asserted-by":"crossref","unstructured":"Bougleux, S., Brun, L., Carletti, V., Foggia, P., Ga\u00fcz\u00e8re, B., Vento, M.: Graph edit distance as a quadratic assignment problem. Pattern Recogn. Lett. 87, 38\u201346 (2017). Advances in Graph-Based Pattern Recognition","DOI":"10.1016\/j.patrec.2016.10.001"},{"key":"18_CR7","unstructured":"Brem, H., Stein, A.B., Rosenkranz, H.S.: The mutagenicity and DNA-modifying effect of haloalkanes. Cancer Res. 34(10), 2576\u20132579 (1974)"},{"key":"18_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"996","DOI":"10.1007\/978-3-540-89689-0_103","volume-title":"Structural, Syntactic, and Statistical Pattern Recognition","author":"H Bunke","year":"2008","unstructured":"Bunke, H., Riesen, K.: Graph classification based on dissimilarity space embedding. In: da Vitoria Lobo, N., et al. (eds.) SSPR \/SPR 2008. LNCS, vol. 5342, pp. 996\u20131007. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-89689-0_103"},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Bunke, H., Riesen, K.: Graph edit distance\u2013optimal and suboptimal algorithms with applications. In: Analysis of Complex Networks: From Biology to Linguistics, pp. 113\u2013143 (2009)","DOI":"10.1002\/9783527627981.ch6"},{"key":"18_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1007\/978-3-319-18224-7_19","volume-title":"Graph-Based Representations in Pattern Recognition","author":"V Carletti","year":"2015","unstructured":"Carletti, V., Ga\u00fcz\u00e8re, B., Brun, L., Vento, M.: Approximate graph edit distance computation combining bipartite matching and exact neighborhood substructure distance. In: Liu, C.-L., Luo, B., Kropatsch, W.G., Cheng, J. (eds.) GbRPR 2015. LNCS, vol. 9069, pp. 188\u2013197. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-18224-7_19"},{"key":"18_CR11","series-title":"Methods in Molecular Biology","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/978-1-0716-0826-5_3","volume-title":"Artificial Neural Networks","author":"D Chicco","year":"2021","unstructured":"Chicco, D.: Siamese neural networks: an overview. In: Cartwright, H. (ed.) Artificial Neural Networks. MMB, vol. 2190, pp. 73\u201394. Springer, New York (2021). https:\/\/doi.org\/10.1007\/978-1-0716-0826-5_3"},{"key":"18_CR12","unstructured":"Chithrananda, S., Grand, G., Ramsundar, B.: ChemBERTa: large-scale self-supervised pretraining for molecular property prediction. arXiv preprint arXiv:2010.09885 (2020)"},{"key":"18_CR13","doi-asserted-by":"crossref","unstructured":"Doan, K.D., Manchanda, S., Mahapatra, S., Reddy, C.K.: Interpretable graph similarity computation via differentiable optimal alignment of node embeddings. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 665\u2013674 (2021)","DOI":"10.1145\/3404835.3462960"},{"key":"18_CR14","doi-asserted-by":"crossref","unstructured":"Eklund, A.C., Friis, P., Wernersson, R., Szallasi, Z.: Optimization of the BLASTN substitution matrix for prediction of non-specific DNA microarray hybridization. Nucleic Acids Res. 38(4), e27\u2013e27 (2010)","DOI":"10.1093\/nar\/gkp1116"},{"key":"18_CR15","unstructured":"Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: International Conference on Machine Learning, pp. 1263\u20131272. PMLR (2017)"},{"key":"18_CR16","doi-asserted-by":"crossref","unstructured":"Gligorijevi\u0107, V., et al.: Structure-based protein function prediction using graph convolutional networks. Nat. Commun. 12(1), 3168 (2021)","DOI":"10.1038\/s41467-021-23303-9"},{"key":"18_CR17","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/S0065-227X(99)80007-0","volume":"36","author":"O Gotoh","year":"1999","unstructured":"Gotoh, O.: Multiple sequence alignment: algorithms and applications. Adv. Biophys. 36, 159\u2013206 (1999)","journal-title":"Adv. Biophys."},{"key":"18_CR18","doi-asserted-by":"crossref","unstructured":"Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201906), vol.\u00a02, pp. 1735\u20131742. IEEE (2006)","DOI":"10.1109\/CVPR.2006.100"},{"key":"18_CR19","doi-asserted-by":"crossref","unstructured":"Henikoff, S., Henikoff, J.G.: Amino acid substitution matrices. Adv. Protein Chem. 54, 73\u201398 (2000)","DOI":"10.1016\/S0065-3233(00)54003-0"},{"key":"18_CR20","doi-asserted-by":"crossref","unstructured":"Heo, J., Lee, S., Ahn, S., Kim, D.: EPIC: graph augmentation with edit path interpolation via learnable cost. arXiv preprint arXiv:2306.01310 (2023)","DOI":"10.24963\/ijcai.2024\/455"},{"key":"18_CR21","doi-asserted-by":"crossref","unstructured":"Hofacker, I.L., Bernhart, S.H.F., Stadler, P.F.: Alignment of RNA base pairing probability matrices. Bioinformatics 20(14), 2222\u20132227 (2004)","DOI":"10.1093\/bioinformatics\/bth229"},{"key":"18_CR22","doi-asserted-by":"crossref","unstructured":"Illerg\u00e5rd, K., Ardell, D.H., Elofsson, A.: Structure is three to ten times more conserved than sequence-a study of structural response in protein cores. Proteins Struct. Function Bioinform. 77(3), 499\u2013508 (2009)","DOI":"10.1002\/prot.22458"},{"key":"18_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-017-1703-z","volume":"18","author":"F Keul","year":"2017","unstructured":"Keul, F., Hess, M., Goesele, M., Hamacher, K.: PFASUM: a substitution matrix from PFAM structural alignments. BMC Bioinform. 18, 1\u201314 (2017)","journal-title":"BMC Bioinform."},{"key":"18_CR24","doi-asserted-by":"crossref","unstructured":"Koshi, J.M., Goldstein, R.A.: Context-dependent optimal substitution matrices. Protein Eng. Des. Sel. 8(7), 641\u2013645 (1995)","DOI":"10.1093\/peds\/8.7.641"},{"key":"18_CR25","unstructured":"Kucera, T., Oliver, C., Chen, D., Borgwardt, K.: ProteinShake: building datasets and benchmarks for deep learning on protein structures. In: Thirty-Seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2023)"},{"issue":"2","key":"18_CR26","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1093\/bioinformatics\/btz595","volume":"36","author":"M Kulmanov","year":"2020","unstructured":"Kulmanov, M., Hoehndorf, R.: DeepGOPlus: improved protein function prediction from sequence. Bioinformatics 36(2), 422\u2013429 (2020)","journal-title":"Bioinformatics"},{"key":"18_CR27","unstructured":"Li, Y., Gu, C., Dullien, T., Vinyals, O., Kohli, P.: Graph matching networks for learning the similarity of graph structured objects. In: International Conference on Machine Learning, pp. 3835\u20133845. PMLR (2019)"},{"issue":"1","key":"18_CR28","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1038\/s41592-022-01700-2","volume":"20","author":"F Llinares-L\u00f3pez","year":"2023","unstructured":"Llinares-L\u00f3pez, F., Berthet, Q., Blondel, M., Teboul, O., Vert, J.-P.: Deep embedding and alignment of protein sequences. Nat. Methods 20(1), 104\u2013111 (2023)","journal-title":"Nat. Methods"},{"key":"18_CR29","doi-asserted-by":"crossref","unstructured":"Mallet, V., Oliver, C., Broadbent, J., Hamilton, W.L., Waldisp\u00fchl, J.: RNAglib: a Python package for RNA 2.5 D graphs. Bioinformatics 38(5), 1458\u20131459 (2022)","DOI":"10.1093\/bioinformatics\/btab844"},{"key":"18_CR30","unstructured":"Morris, C., Kriege, N.M., Bause, F., Kersting, K., Mutzel, P., Neumann, M.: Tudataset: a collection of benchmark datasets for learning with graphs. arXiv preprint arXiv:2007.08663 (2020)"},{"key":"18_CR31","doi-asserted-by":"crossref","unstructured":"Neuhaus, M., Bunke, H.: A probabilistic approach to learning costs for graph edit distance. In: Proceedings of the 17th International Conference on Pattern Recognition. ICPR 2004, vol. 3, pp. 389\u2013393. IEEE (2004)","DOI":"10.1109\/ICPR.2004.1334548"},{"key":"18_CR32","doi-asserted-by":"crossref","unstructured":"Oliver, C., et al.: Augmented base pairing networks encode RNA-small molecule binding preferences. Nucleic Acids Res. 48(14), 7690\u20137699 (2020)","DOI":"10.1093\/nar\/gkaa583"},{"key":"18_CR33","doi-asserted-by":"crossref","unstructured":"Porter, C.T., Bartlett, G.J., Thornton, J.M.: The catalytic site atlas: a resource of catalytic sites and residues identified in enzymes using structural data. Nucleic Acids Res. 32(suppl_1), D129\u2013D133 (2004)","DOI":"10.1093\/nar\/gkh028"},{"key":"18_CR34","doi-asserted-by":"crossref","unstructured":"Qiu, J., Elber, R.: SSALN: an alignment algorithm using structure-dependent substitution matrices and gap penalties learned from structurally aligned protein pairs. Proteins Struct Function Bioinform. 62(4), 881\u2013891 (2006)","DOI":"10.1002\/prot.20854"},{"key":"18_CR35","unstructured":"Ranjan, R., Grover, S., Medya, S., Chakaravarthy, V., Sabharwal, Y., Ranu, S.: Greed: a neural framework for learning graph distance functions. In: Advances in Neural Information Processing Systems, vol. 35, pp. 22518\u201322530 (2022)"},{"key":"18_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108132","volume":"120","author":"P Riba","year":"2021","unstructured":"Riba, P., Fischer, A., Llad\u00f3s, J., Forn\u00e9s, A.: Learning graph edit distance by graph neural networks. Pattern Recogn. 120, 108132 (2021)","journal-title":"Pattern Recogn."},{"key":"18_CR37","doi-asserted-by":"crossref","unstructured":"Rose, P.W., et al.: The RCSB protein data bank: new resources for research and education. Nucleic Acids Res. 41(D1), D475\u2013D482 (2012)","DOI":"10.1093\/nar\/gks1200"},{"key":"18_CR38","doi-asserted-by":"crossref","unstructured":"Sarver, M., Zirbel, C.L., Stombaugh, J., Mokdad, A., Leontis, N.B.: FR3D: finding local and composite recurrent structural motifs in RNA 3D structures. J. Math. Biol. 56, 215\u2013252 (2008)","DOI":"10.1007\/s00285-007-0110-x"},{"key":"18_CR39","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815\u2013823 (2015)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"18_CR40","doi-asserted-by":"crossref","unstructured":"Sheridan, R.P.: The most common chemical replacements in drug-like compounds. J. Chem. Inf. Comput. Sci. 42(1), 103\u2013108 (2002)","DOI":"10.1021\/ci0100806"},{"key":"18_CR41","unstructured":"Shervashidze, N., Schweitzer, P., Van\u00a0Leeuwen, E.J., Mehlhorn, K., Borgwardt, K.M.: Weisfeiler-Lehman graph kernels. J. Mach. Learn. Res. 12(9) (2011)"},{"key":"18_CR42","doi-asserted-by":"crossref","unstructured":"Song, D., et al.: Parameterized blosum matrices for protein alignment. IEEE\/ACM Trans. Comput. Biol. Bioinform. 12(3), 686\u2013694 (2014)","DOI":"10.1109\/TCBB.2014.2366126"},{"key":"18_CR43","doi-asserted-by":"crossref","unstructured":"Sutormin, R.A., Rakhmaninova, A.B., Gelfand, M.S.: Batmas30: amino acid substitution matrix for alignment of bacterial transporters. Proteins Struct Function Bioinform. 51(1), 85\u201395 (2003)","DOI":"10.1002\/prot.10308"},{"issue":"3","key":"18_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/gb-2007-8-3-r31","volume":"8","author":"C-H Tung","year":"2007","unstructured":"Tung, C.-H., Huang, J.-W., Yang, J.-M.: Kappa-alpha plot derived structural alphabet and blosum-like substitution matrix for rapid search of protein structure database. Genome Biol. 8(3), 1\u201316 (2007)","journal-title":"Genome Biol."},{"key":"18_CR45","doi-asserted-by":"crossref","unstructured":"van Kempen, M., et al.: Fast and accurate protein structure search with Foldseek. Nat. Biotechnol. pp. 1\u20134 (2023)","DOI":"10.1101\/2022.02.07.479398"},{"key":"18_CR46","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)"},{"key":"18_CR47","unstructured":"Wilbur, W.J.: On the PAM matrix model of protein evolution. Mol. Biol. Evol. 2(5), 434\u2013447 (1985)"},{"key":"18_CR48","doi-asserted-by":"crossref","unstructured":"Wu, C.-Y., Manmatha, R., Smola, A.J., Krahenbuhl, P.: Sampling matters in deep embedding learning. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2840\u20132848 (2017)","DOI":"10.1109\/ICCV.2017.309"},{"key":"18_CR49","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: International Conference on Learning Representations (2018)"}],"container-title":["Lecture Notes in Computer Science","Research in Computational Molecular Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-1-0716-3989-4_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T05:02:41Z","timestamp":1731992561000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-1-0716-3989-4_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9781071639887","9781071639894"],"references-count":49,"URL":"https:\/\/doi.org\/10.1007\/978-1-0716-3989-4_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"17 May 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"RECOMB","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Research in Computational Molecular Biology","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cambridge, MA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","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":"29 April 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 May 2024","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":"recomb2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/recomb.org\/recomb2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}