{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T05:23:40Z","timestamp":1761110620144,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":30,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811684296"},{"type":"electronic","value":"9789811684302"}],"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.springer.com\/tdm"},{"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.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-16-8430-2_15","type":"book-chapter","created":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T18:02:42Z","timestamp":1641319362000},"page":"159-168","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Prediction of Drug-Gene Interaction by Using Biomedical Subgraph Patterns"],"prefix":"10.1007","author":[{"given":"Guangjin","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meijing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingying","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,4]]},"reference":[{"key":"15_CR1","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1016\/j.ccell.2020.10.014","volume":"38","author":"CS Greene","year":"2020","unstructured":"Greene, C.S., Costello, J.C.: Biologically informed neural networks predict drug responses. Cancer Cell 38, 613\u2013615 (2020)","journal-title":"Cancer Cell"},{"key":"15_CR2","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1039\/C7MB00059F","volume":"13","author":"Y Hwang","year":"2017","unstructured":"Hwang, Y., et al.: Identifying the common genetic networks of ADR (adverse drug reaction) clusters and developing an ADR classification model. Mol. Biosyst. 13, 1788\u20131796 (2017)","journal-title":"Mol. Biosyst."},{"key":"15_CR3","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.molonc.2012.01.008","volume":"6","author":"PT Ram","year":"2012","unstructured":"Ram, P.T., Mendelsohn, J., Mills, G.B.: Bioinformatics and systems biology. Mol. Oncol. 6, 147\u2013154 (2012)","journal-title":"Mol. Oncol."},{"issue":"2","key":"15_CR4","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/s10928-019-09626-7","volume":"46","author":"LZ Benet","year":"2019","unstructured":"Benet, L.Z., Bowman, C.M., Koleske, M.L., Rinaldi, C.L., Sodhi, J.K.: Understanding drug\u2013drug interaction and pharmacogenomic changes in pharmacokinetics for metabolized drugs. J. Pharmacokinet. Pharmacodyn. 46(2), 155\u2013163 (2019). https:\/\/doi.org\/10.1007\/s10928-019-09626-7","journal-title":"J. Pharmacokinet. Pharmacodyn."},{"key":"15_CR5","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1016\/j.drudis.2014.10.012","volume":"20","author":"A Lavecchia","year":"2015","unstructured":"Lavecchia, A.: Machine-learning approaches in drug discovery: methods and applications. Drug Discov. Today 20, 318\u2013331 (2015)","journal-title":"Drug Discov. Today"},{"key":"15_CR6","doi-asserted-by":"crossref","unstructured":"Malki, M., Pearson, E.: Drug\u2013drug\u2013gene interactions and adverse drug reactions. Pharmacogenomics J. 20, 1\u201312 (2019)","DOI":"10.1038\/s41397-019-0122-0"},{"key":"15_CR7","doi-asserted-by":"crossref","unstructured":"Siyi, Z., Jiaxin, B., Xiaoping, M., Chen, L., Xiangxiang, Z.: Prediction of drug-gene interaction by using Metapath2vec. Front. Genet. 9, 248 (2018)","DOI":"10.3389\/fgene.2018.00248"},{"key":"15_CR8","unstructured":"Westervelt, P., Cho, K., Bright, D., Kisor, D.: Drug\u2013gene interactions: inherent variability in drug maintenance dose requirements. Pharm. Ther. 39, 630\u2013637 (2014)"},{"key":"15_CR9","doi-asserted-by":"crossref","unstructured":"Xuan, P., Sun, C., Zhang, T., Ye, Y., Shen, T., Dong, Y.: Gradient boosting decision tree-based method for predicting interactions between target genes and drugs. Front. Genet. 10, 459 (2013)","DOI":"10.3389\/fgene.2019.00459"},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"Raja, K., Patrick, M., Elder, J.T., Tsoi, L.C.: Machine learning workflow to enhance predictions of adverse drug reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseases. Sci. Rep. 7, 3690 (2017)","DOI":"10.1038\/s41598-017-03914-3"},{"key":"15_CR11","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.3389\/fphar.2018.01134","volume":"9","author":"Z Wu","year":"2018","unstructured":"Wu, Z., Li, W., Liu, G., Tang, Y.: Network-based methods for prediction of drug-target interactions. Front. Pharmacol. 9, 1134 (2018)","journal-title":"Front. Pharmacol."},{"issue":"6","key":"15_CR12","doi-asserted-by":"publisher","first-page":"830","DOI":"10.1093\/bioinformatics\/btaa880","volume":"37","author":"K Huang","year":"2020","unstructured":"Huang, K., Xiao, C., Glass, L.M., Sun, J.: MolTrans: molecular interaction transformer for drug\u2013target interaction prediction. Bioinformatics 37(6), 830\u2013836 (2020)","journal-title":"Bioinformatics"},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"Qiao, F., Pei, L., Xin, Z., Ding, Z., Cheng, J., Wang, H.: Predicting social unrest events with hidden markov models using GDELT. Discrete Dyn. Nat. Soc. 2017, 1\u201313 (2017)","DOI":"10.1155\/2017\/8180272"},{"key":"15_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/3915036","volume":"2020","author":"F Qiao","year":"2020","unstructured":"Qiao, F., Zhang, X., Deng, J.: Learning evolutionary stages with hidden semi-markov model for predicting social unrest events. Discrete Dyn. Nat. Soc. 2020, 1\u201316 (2020)","journal-title":"Discrete Dyn. Nat. Soc."},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"Lee, K., Jung, H., Hong, J.S., Kim, W.: Learning knowledge using frequent subgraph mining from ontology graph data. Appl. Sci. 11, 932 (2021)","DOI":"10.3390\/app11030932"},{"key":"15_CR16","doi-asserted-by":"publisher","first-page":"3287","DOI":"10.1109\/TIT.2020.2996543","volume":"67","author":"B Berger","year":"2021","unstructured":"Berger, B., Waterman, M.S., Yu, Y.W.: Levenshtein distance, sequence comparison and biological database search. IEEE Trans. Inf. Theory 67, 3287\u20133294 (2021)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Rozinek, O., Mare, J.: The duality of similarity and metric spaces. Appl. Sci. 11, 1910 (2021)","DOI":"10.3390\/app11041910"},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"Ramraj, T., Prabhakar, R.: Frequent subgraph mining algorithms \u2013 a survey. Procedia Comput. Sci. 47, 197\u2013204 (2015)","DOI":"10.1016\/j.procs.2015.03.198"},{"key":"15_CR19","unstructured":"Xian-Tong, L.I., Jian-Zhong, L.I., Gao, H.: An efficient frequent subgraph mining algorithm. 18, 2469\u20132480 (2007)"},{"key":"15_CR20","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/978-1-84882-983-1_2","volume-title":"Research and Development in Intelligent Systems XXVI","author":"C Jiang","year":"2010","unstructured":"Jiang, C., Coenen, F., Sanderson, R., Zito, M.: Text classification using graph mining-based feature extraction. In: Bramer, M., Ellis, R., Petridis, M. (eds.) Research and Development in Intelligent Systems XXVI, pp. 21\u201334. Springer, London (2010). https:\/\/doi.org\/10.1007\/978-1-84882-983-1_2"},{"issue":"3","key":"15_CR21","doi-asserted-by":"publisher","first-page":"159","DOI":"10.26599\/BDMA.2019.9020006","volume":"2","author":"BS Jena","year":"2019","unstructured":"Jena, B.S., Khan, C., Sunderraman, R.: High performance frequent subgraph mining on transaction datasets: a survey and performance comparison. Big Data Min. Anal. 2(3), 159\u2013180 (2019)","journal-title":"Big Data Min. Anal."},{"key":"15_CR22","doi-asserted-by":"crossref","unstructured":"Kashyap, N.K., Pandey, B.K., Mandoria, H.L., Kumar, A.: Graph mining using gSpan: graph-based substructure pattern mining. Int. J. Appl. Res. Info. Tech. Comput. 7, 132\u2013139 (2016)","DOI":"10.5958\/0975-8089.2016.00014.2"},{"key":"15_CR23","doi-asserted-by":"crossref","unstructured":"You, Y., Sun, J., Chen, Y.W., Niu, C., Jiang, J.: Ensemble belief rule-based model for complex system classification and prediction. Expert Syst. Appl. 164, 113952 (2020)","DOI":"10.1016\/j.eswa.2020.113952"},{"key":"15_CR24","doi-asserted-by":"crossref","unstructured":"Cotto, K.C., et al.: DGIdb 3.0: a redesign and expansion of the drug-gene interaction database. Nucleic Acids Res. 46(D1), D1068\u2013D1073 (2017)","DOI":"10.1093\/nar\/gkx1143"},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"Freshour, S.L., Kiwala, S., Cotto, K.C., Coffman, A.C., Wagner, A.H.: Integration of the drug\u2013gene interaction database (DGIdb 4.0) with open crowdsource efforts. Nucleic Acids Res. 49, D1144\u2013D1151 (2020)","DOI":"10.1093\/nar\/gkaa1084"},{"key":"15_CR26","doi-asserted-by":"crossref","unstructured":"Wagner, A.H., et al.: DGIdb 2.0: mining clinically relevant drug-gene interactions. Nucleic Acids Res. 44, D1036\u20131044 (2016)","DOI":"10.1093\/nar\/gkv1165"},{"key":"15_CR27","doi-asserted-by":"crossref","unstructured":"Yang, J., et al.: Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 1\u20133 (2012)","DOI":"10.1038\/ng.2213"},{"key":"15_CR28","doi-asserted-by":"publisher","first-page":"D907","DOI":"10.1093\/nar\/gku1066","volume":"43","author":"MC Cai","year":"2015","unstructured":"Cai, M.C., et al.: ADReCS: an ontology database for aiding standardization and hierarchical classification of adverse drug reaction terms. Nucleic Acids Res. 43, D907\u2013D913 (2015)","journal-title":"Nucleic Acids Res."},{"issue":"9","key":"15_CR29","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1002\/jez.a.307","volume":"305A","author":"CJ Mattingly","year":"2006","unstructured":"Mattingly, C.J., Rosenstein, M.C., Colby, G.T., Forrest, J.N., Jr., Boyer, J.L.: The Comparative Toxicogenomics database (CTD): a resource for comparative toxicological studies. J. Exp. Zool. Part A Comp. Exp. Biol. 305A(9), 689\u2013692 (2006)","journal-title":"J. Exp. Zool. Part A Comp. Exp. Biol."},{"key":"15_CR30","doi-asserted-by":"crossref","unstructured":"Moses, L.E., Shapiro, D., Littenberg, B.: Combining independent studies of a diagnostic test into a summary ROC curve: data-analytic approaches and some additional considerations. Stat. Med. 12, 1293\u20131316 (2010)","DOI":"10.1002\/sim.4780121403"}],"container-title":["Lecture Notes in Electrical Engineering","Genetic and Evolutionary Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-8430-2_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,5]],"date-time":"2022-05-05T17:07:56Z","timestamp":1651770476000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-8430-2_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811684296","9789811684302"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-8430-2_15","relation":{},"ISSN":["1876-1100","1876-1119"],"issn-type":[{"type":"print","value":"1876-1100"},{"type":"electronic","value":"1876-1119"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"4 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICGEC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Genetic and Evolutionary Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jilin City","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"21 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icgec2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}