{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:51:13Z","timestamp":1742914273794,"version":"3.40.3"},"publisher-location":"Cham","reference-count":49,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030681531"},{"type":"electronic","value":"9783030681548"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-68154-8_71","type":"book-chapter","created":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T04:47:45Z","timestamp":1612846065000},"page":"823-837","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Graph Neural Networks in Cheminformatics"],"prefix":"10.1007","author":[{"given":"H. N. Tran","family":"Tran","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J.","family":"Joshua Thomas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nurul Hashimah Ahamed Hassain","family":"Malim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdalla M.","family":"Ali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Son Bach","family":"Huynh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,2,8]]},"reference":[{"key":"71_CR1","doi-asserted-by":"publisher","unstructured":"Joshua Thomas, J., Pillai, N.: A deep learning framework on generation of image descriptions with bidirectional recurrent neural networks. In: Advances in Intelligent Systems and Computing, vol. 866. Springer International Publishing (2019). https:\/\/doi.org\/10.1007\/978-3-030-00979-3_22","DOI":"10.1007\/978-3-030-00979-3_22"},{"issue":"November","key":"71_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/frobt.2019.00108","volume":"6","author":"CF Lipinski","year":"2019","unstructured":"Lipinski, C.F., Maltarollo, V.G., Oliveira, P.R., da Silva, A.B.F., Honorio, K.M.: Advances and perspectives in applying deep learning for drug design and discovery. Front. Robot. AI 6(November), 1\u20136 (2019). https:\/\/doi.org\/10.3389\/frobt.2019.00108","journal-title":"Front. Robot. AI"},{"issue":"6","key":"71_CR3","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1016\/j.drudis.2018.01.039","volume":"23","author":"H Chen","year":"2018","unstructured":"Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., Blaschke, T.: The rise of deep learning in drug discovery. Drug Discov. Today 23(6), 1241\u20131250 (2018). https:\/\/doi.org\/10.1016\/j.drudis.2018.01.039","journal-title":"Drug Discov. Today"},{"key":"71_CR4","unstructured":"Li, J., Cai, D., He, X.: Learning Graph-Level Representation for Drug Discovery (2017). http:\/\/arxiv.org\/abs\/1709.03741"},{"key":"71_CR5","doi-asserted-by":"publisher","unstructured":"Lo, Y.C., Rensi, S.E., Torng, W., Altman, R.B.: Machine learning in chemoinformatics and drug discovery. Drug Discov. Today, 23(8), 1538\u20131546. Elsevier Ltd. (2018). https:\/\/doi.org\/10.1016\/j.drudis.2018.05.010","DOI":"10.1016\/j.drudis.2018.05.010"},{"issue":"8","key":"71_CR6","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1016\/j.tips.2019.06.004","volume":"40","author":"HCS Chan","year":"2019","unstructured":"Chan, H.C.S., Shan, H., Dahoun, T., Vogel, H., Yuan, S.: Advancing drug discovery via artificial intelligence. Trends Pharmacol. Sci. 40(8), 592\u2013604 (2019). https:\/\/doi.org\/10.1016\/j.tips.2019.06.004","journal-title":"Trends Pharmacol. Sci."},{"issue":"5","key":"71_CR7","doi-asserted-by":"publisher","first-page":"1878","DOI":"10.1093\/bib\/bby061","volume":"20","author":"AS Rifaioglu","year":"2019","unstructured":"Rifaioglu, A.S., Atas, H., Martin, M.J., Cetin-Atalay, R., Atalay, V., Do\u01e7an, T.: Recent applications of deep learning and machine intelligence on in silico drug discovery: Methods, tools and databases. Brief. Bioinform. 20(5), 1878\u20131912 (2019). https:\/\/doi.org\/10.1093\/bib\/bby061","journal-title":"Brief. Bioinform."},{"issue":"9","key":"71_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/molecules23092208","volume":"23","author":"R Chen","year":"2018","unstructured":"Chen, R., Liu, X., Jin, S., Lin, J., Liu, J.: Machine learning for drug-target interaction prediction. Molecules 23(9), 1\u201315 (2018). https:\/\/doi.org\/10.3390\/molecules23092208","journal-title":"Molecules"},{"key":"71_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fphar.2019.01592","volume":"10","author":"H Wang","year":"2020","unstructured":"Wang, H., Wang, J., Dong, C., Lian, Y., Liu, D., Yan, Z.: A novel approach for drug-target interactions prediction based on multimodal deep autoencoder. Front. Pharmacol. 10, 1\u201319 (2020). https:\/\/doi.org\/10.3389\/fphar.2019.01592","journal-title":"Front. Pharmacol."},{"issue":"5","key":"71_CR10","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1093\/bib\/bbt056","volume":"15","author":"H Ding","year":"2013","unstructured":"Ding, H., Takigawa, I., Mamitsuka, H., Zhu, S.: Similarity-based machine learning methods for predicting drug-target interactions: A brief review. Brief. Bioinform. 15(5), 734\u2013747 (2013). https:\/\/doi.org\/10.1093\/bib\/bbt056","journal-title":"Brief. Bioinform."},{"key":"71_CR11","doi-asserted-by":"publisher","unstructured":"Sachdev, K., Gupta, M.K.: A comprehensive review of feature-based methods for drug target interaction prediction. J. Biomed. Inf. Elsevier (2019). https:\/\/doi.org\/10.1016\/j.jbi.2019.103159","DOI":"10.1016\/j.jbi.2019.103159"},{"key":"71_CR12","doi-asserted-by":"publisher","unstructured":"Thomas, J.J., Ali, A.M.: Dispositional learning analytics structure integrated with recurrent neural networks in predicting students performance. In: Vasant, P., Zelinka, I., Weber, G.W. (eds.) Intelligent Computing and Optimization. ICO 2019. Advances in Intelligent Systems and Computing, vol 1072. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-33585-4_44","DOI":"10.1007\/978-3-030-33585-4_44"},{"key":"71_CR13","unstructured":"Dahl, G.E., Jaitly, N., Salakhutdinov, R. Multi-task Neural Networks for QSAR Predictions, pp. 1\u201321 (2014). http:\/\/arxiv.org\/abs\/1406.1231"},{"key":"71_CR14","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.ymeth.2016.06.024","volume":"110","author":"K Tian","year":"2016","unstructured":"Tian, K., Shao, M., Wang, Y., Guan, J., Zhou, S.: Boosting compound-protein interaction prediction by deep learning. Methods 110, 64\u201372 (2016). https:\/\/doi.org\/10.1016\/j.ymeth.2016.06.024","journal-title":"Methods"},{"issue":"1","key":"71_CR15","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1021\/acscentsci.7b00512","volume":"4","author":"MHS Segler","year":"2018","unstructured":"Segler, M.H.S., Kogej, T., Tyrchan, C., Waller, M.P.: Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Central Sci. 4(1), 120\u2013131 (2018). https:\/\/doi.org\/10.1021\/acscentsci.7b00512","journal-title":"ACS Central Sci."},{"key":"71_CR16","doi-asserted-by":"publisher","unstructured":"Gupta, A., M\u00fcller, A.T., Huisman, B.J.H., Fuchs, J.A., Schneider, P., Schneider, G.: Generative recurrent networks for De Novo drug design. Mol. Inf. 37(1) (2018). https:\/\/doi.org\/10.1002\/minf.201700111","DOI":"10.1002\/minf.201700111"},{"issue":"Suppl 19","key":"71_CR17","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1186\/s12859-018-2523-5","volume":"19","author":"M Hirohara","year":"2018","unstructured":"Hirohara, M., Saito, Y., Koda, Y., Sato, K., Sakakibara, Y.: Convolutional neural network based on SMILES representation of compounds for detecting chemical motif. BMC Bioinf. 19(Suppl 19), 83\u201394 (2018). https:\/\/doi.org\/10.1186\/s12859-018-2523-5","journal-title":"BMC Bioinf."},{"issue":"6","key":"71_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pcbi.1007129","volume":"15","author":"I Lee","year":"2019","unstructured":"Lee, I., Keum, J., Nam, H.: DeepConv-DTI: prediction of drug-target interactions via deep learning with convolution on protein sequences. PLoS Comput. Biol. 15(6), 1\u201321 (2019). https:\/\/doi.org\/10.1371\/journal.pcbi.1007129","journal-title":"PLoS Comput. Biol."},{"issue":"17","key":"71_CR19","doi-asserted-by":"publisher","first-page":"i821","DOI":"10.1093\/bioinformatics\/bty593","volume":"34","author":"H \u00d6zt\u00fcrk","year":"2018","unstructured":"\u00d6zt\u00fcrk, H., \u00d6zg\u00fcr, A., Ozkirimli, E.: DeepDTA: Deep drug-target binding affinity prediction. Bioinformatics 34(17), i821\u2013i829 (2018). https:\/\/doi.org\/10.1093\/bioinformatics\/bty593","journal-title":"Bioinformatics"},{"issue":"14","key":"71_CR20","doi-asserted-by":"publisher","first-page":"i269","DOI":"10.1093\/bioinformatics\/btz339","volume":"35","author":"A Trabelsi","year":"2019","unstructured":"Trabelsi, A., Chaabane, M., Ben-Hur, A.: Comprehensive evaluation of deep learning architectures for prediction of DNA\/RNA sequence binding specificities. Bioinformatics 35(14), i269\u2013i277 (2019). https:\/\/doi.org\/10.1093\/bioinformatics\/btz339","journal-title":"Bioinformatics"},{"issue":"18","key":"71_CR21","doi-asserted-by":"publisher","first-page":"3329","DOI":"10.1093\/bioinformatics\/btz111","volume":"35","author":"M Karimi","year":"2019","unstructured":"Karimi, M., Wu, D., Wang, Z., Shen, Y.: DeepAffinity: Interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks. Bioinformatics 35(18), 3329\u20133338 (2019). https:\/\/doi.org\/10.1093\/bioinformatics\/btz111","journal-title":"Bioinformatics"},{"key":"71_CR22","doi-asserted-by":"publisher","unstructured":"Samanta, B., De, A., Jana, G., Chattaraj, P.K., Ganguly, N., Rodriguez, M.G.: NeVAE: a deep generative model for molecular graphs. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 1110\u20131117 (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.33011110","DOI":"10.1609\/aaai.v33i01.33011110"},{"issue":"4","key":"71_CR23","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1039\/c9sc04503a","volume":"11","author":"J Lim","year":"2019","unstructured":"Lim, J., Hwang, S.Y., Moon, S., Kim, S., Kim, W.Y.: Scaffold-based molecular design with a graph generative model. Chem. Sci. 11(4), 1153\u20131164 (2019). https:\/\/doi.org\/10.1039\/c9sc04503a","journal-title":"Chem. Sci."},{"issue":"2","key":"71_CR24","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1021\/acscentsci.7b00572","volume":"4","author":"R G\u00f3mez-Bombarelli","year":"2018","unstructured":"G\u00f3mez-Bombarelli, R., Wei, J.N., Duvenaud, D., Hern\u00e1ndez-Lobato, J.M., S\u00e1nchezLengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T.D., Adams, R.P., Aspuru-Guzik, A.: Automatic chemical design using a data-driven continuous representation of molecules. ACS Central Sci. 4(2), 268\u2013276 (2018). https:\/\/doi.org\/10.1021\/acscentsci.7b00572","journal-title":"ACS Central Sci."},{"issue":"4","key":"71_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/biom8040131","volume":"8","author":"EJ Bjerrum","year":"2018","unstructured":"Bjerrum, E.J., Sattarov, B.: Improving chemical autoencoder latent space and molecular de novo generation diversity with heteroencoders. Biomolecules 8(4), 1\u201313 (2018). https:\/\/doi.org\/10.3390\/biom8040131","journal-title":"Biomolecules"},{"issue":"1","key":"71_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-018-0286-7","volume":"10","author":"J Lim","year":"2018","unstructured":"Lim, J., Ryu, S., Kim, J.W., Kim, W.Y.: Molecular generative model based on conditional variational autoencoder for de novo molecular design. J. Cheminform. 10(1), 1\u20139 (2018). https:\/\/doi.org\/10.1186\/s13321-018-0286-7","journal-title":"J. Cheminform."},{"key":"71_CR27","doi-asserted-by":"publisher","unstructured":"Sattarov, B., Baskin, I.I., Horvath, D., Marcou, G., Bjerrum, E.J., Varnek, A.: De Novo molecular design by combining deep autoencoder recurrent neural networks with generative topographic mapping. J. Chem. Inf. Model. 59(3), 1182\u20131196. Research-Article (2019). https:\/\/doi.org\/10.1021\/acs.jcim.8b00751","DOI":"10.1021\/acs.jcim.8b00751"},{"issue":"1","key":"71_CR28","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2009","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Networks 20(1), 61\u201380 (2009)","journal-title":"IEEE Trans. Neural Networks"},{"key":"71_CR29","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P.S.: A Comprehensive Survey on Graph Neural Networks, pp. 1\u201322 (2019). http:\/\/arxiv.org\/abs\/1901.00596"},{"key":"71_CR30","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings, pp. 1\u201314 (2017). https:\/\/arxiv.org\/abs\/1609.02907"},{"key":"71_CR31","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, pp. 1024\u20131034 (2017)"},{"key":"71_CR32","unstructured":"Li, Y., Zemel, R., Brockschmidt, M., Tarlow, D.: Gated graph sequence neural networks. In: 4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings, pp. 1\u201320 (2016)"},{"key":"71_CR33","unstructured":"You, J., Ying, R., Ren, X., Hamilton, W.L., Leskovec, J.: GraphRNN: generating realistic graphs with deep auto-regressive models. In: 35th International Conference on Machine Learning, ICML 2018, vol. 13, pp. 9072\u20139081 (2018)"},{"key":"71_CR34","unstructured":"Popova, M., Shvets, M., Oliva, J., Isayev, O.: MolecularRNN: Generating realistic molecular graphs with optimized properties (2019). https:\/\/arxiv.org\/abs\/1905.13372"},{"key":"71_CR35","unstructured":"Hajiramezanali, E., Hasanzadeh, A., Duffield, N., Narayanan, K.R., Zhou, M., Qian, X.: Variational Graph Recurrent Neural Networks, pp. 1\u201312 (2019). http:\/\/arxiv.org\/abs\/1908.09710"},{"key":"71_CR36","unstructured":"Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., G\u00f3mez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., Adams, R.P.: Convolutional networks on graphs for learning molecular fingerprints. In: Advances in Neural Information Processing Systems, pp. 2224\u20132232 (2015)"},{"key":"71_CR37","doi-asserted-by":"publisher","unstructured":"Kearnes, S., McCloskey, K., Berndl, M., Pande, V., Riley, P.: Molecular graph convolutions: moving beyond fingerprints. J. Comp. Aided Mol. Des. 30, 595\u2013608 (2016). https:\/\/doi.org\/10.1007\/s10822-016-9938-8","DOI":"10.1007\/s10822-016-9938-8"},{"issue":"2","key":"71_CR38","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1039\/c8sc04228d","volume":"10","author":"CW Coley","year":"2019","unstructured":"Coley, C.W., Jin, W., Rogers, L., Jamison, T.F., Jaakkola, T.S., Green, W.H., Jensen, K.F.: A graph-convolutional neural network model for the prediction of chemical reactivity. Chem. Sci. 10(2), 370\u2013377 (2019). https:\/\/doi.org\/10.1039\/c8sc04228d","journal-title":"Chem. Sci."},{"key":"71_CR39","doi-asserted-by":"publisher","unstructured":"Ryu, S., Lim, J., Hong, S.H., Kim, W.Y.: Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network (2018). https:\/\/doi.org\/10.1039\/b000000x\/been","DOI":"10.1039\/b000000x\/been"},{"issue":"2","key":"71_CR40","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1093\/bioinformatics\/bty535","volume":"35","author":"M Tsubaki","year":"2019","unstructured":"Tsubaki, M., Tomii, K., Sese, J.: Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences. Bioinformatics 35(2), 309\u2013318 (2019). https:\/\/doi.org\/10.1093\/bioinformatics\/bty535","journal-title":"Bioinformatics"},{"key":"71_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1101\/684662","volume":"12","author":"T Nguyen","year":"2019","unstructured":"Nguyen, T., Le, H., Quinn, T.P., Le, T., Venkatesh, S.: Predicting drug\u2013target binding affinity with graph neural networks. BioRxiv 12, 1\u201318 (2019). https:\/\/doi.org\/10.1101\/684662","journal-title":"BioRxiv"},{"key":"71_CR42","doi-asserted-by":"crossref","unstructured":"Thomas, J.J., Tran, H.N.T., Lechuga, G.P., Belaton, B.: Convolutional graph neural networks: a review and applications of graph autoencoder in chemoinformatics. In: Thomas, J.J., Karagoz, P., Ahamed, B.B., Vasant, P. (eds.) Deep Learning Techniques and Optimization Strategies in Big Data Analytics, pp. 107\u2013123. IGI Global (2020). http:\/\/doi.org\/10.4018\/978-1-7998-1192-3.ch007","DOI":"10.4018\/978-1-7998-1192-3.ch007"},{"key":"71_CR43","unstructured":"Niepert, M., Ahmad, M., Kutzkov, K.: Learning convolutional neural networks for graphs. In: 33rd International Conference on Machine Learning, ICML 2016, vol. 4, pp. 2958\u20132967 (2016)"},{"key":"71_CR44","unstructured":"Kusner, M.J., Paige, B., Hem\u00e1ndez-Lobato, J.M.: Grammar variational autoencoder. In: 34th International Conference on Machine Learning, ICML 2017, vol. 4, pp. 3072\u20133084 (2017)"},{"key":"71_CR45","doi-asserted-by":"publisher","unstructured":"Simonovsky, M., Komodakis, N.: GraphVAE: towards generation of small graphs using variational autoencoders. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNCS, vol. 11139, pp. 412\u2013422 (2018). https:\/\/doi.org\/10.1007\/978-3-030-01418-6_41","DOI":"10.1007\/978-3-030-01418-6_41"},{"key":"71_CR46","unstructured":"De Cao, N., Kipf, T.: MolGAN: An implicit generative model for small molecular graphs (2018). http:\/\/arxiv.org\/abs\/1805.11973"},{"key":"71_CR47","unstructured":"Bresson, X., Laurent, T.: A Two-Step Graph Convolutional Decoder for Molecule Generation (2019). http:\/\/arxiv.org\/abs\/1906.03412"},{"key":"71_CR48","doi-asserted-by":"publisher","unstructured":"Lim, J., Ryu, S., Park, K., Choe, Y.J., Ham, J., Kim, W.Y.: Predicting drug-target interaction using a novel graph neural network with 3D structure-embedded graph representation. J. Chem. Inf. Model. 59(9), 3981\u20133988. Research-Article (2019). https:\/\/doi.org\/10.1021\/acs.jcim.9b00387","DOI":"10.1021\/acs.jcim.9b00387"},{"key":"71_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2017.09.007","author":"A Gonczarek","year":"2017","unstructured":"Gonczarek, A., et al.: Interaction prediction in structure-based virtual screening using deep learning. Comput. Biol. Med. (2017). https:\/\/doi.org\/10.1016\/j.compbiomed.2017.09.007","journal-title":"Comput. Biol. Med."}],"container-title":["Advances in Intelligent Systems and Computing","Intelligent Computing and Optimization"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-68154-8_71","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,14]],"date-time":"2021-05-14T17:21:13Z","timestamp":1621012873000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-68154-8_71"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030681531","9783030681548"],"references-count":49,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-68154-8_71","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"8 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICO","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing & Optimization","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Koh Samui","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Thailand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 December 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ico0","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}