{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T22:48:09Z","timestamp":1743115689482,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":27,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819619061"},{"type":"electronic","value":"9789819619078"}],"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\/978-981-96-1907-8_20","type":"book-chapter","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T19:55:11Z","timestamp":1740426911000},"page":"202-213","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Multi-module Hybrid Neural Framework for Transformer-Based Drug Response Prediction"],"prefix":"10.1007","author":[{"given":"Yuanyuan","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongguo","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanmei","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuzhong","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,25]]},"reference":[{"issue":"1","key":"20_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12935-021-01748-8","volume":"21","author":"Y Zheng","year":"2021","unstructured":"Zheng, Y., Sun, Y., Kuai, Y.: Gene expression profiling for the diagnosis of multiple primary malignant tumors. Cancer Cell Int. 21(1), 1\u20139 (2021)","journal-title":"Cancer Cell Int."},{"issue":"2","key":"20_CR2","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1109\/TCBB.2020.3007544","volume":"19","author":"Z Shen","year":"2020","unstructured":"Shen, Z., Zhang, Q., Han, K.: A deep learning model for RNA-protein binding preference prediction based on hierarchical LSTM and attention network. IEEE\/ACM Trans. Comput. Biol. Bioinf. 19(2), 753\u2013762 (2020)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"issue":"13","key":"20_CR3","doi-asserted-by":"publisher","first-page":"4038","DOI":"10.1093\/bioinformatics\/btz825","volume":"36","author":"L Wang","year":"2020","unstructured":"Wang, L., You, Z.H., Huang, Y.A.: An efficient approach based on multi-sources information to predict CircRNA\u2013disease associations using deep convolutional neural network. Bioinformatics 36(13), 4038\u20134046 (2020)","journal-title":"Bioinformatics"},{"issue":"2","key":"20_CR4","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1109\/TCDS.2022.3193398","volume":"15","author":"HL Su","year":"2022","unstructured":"Su, H.L., Li, Z.P., Zhu, X.B.: Hierarchical graph neural network based on semi-implicit variational inference. IEEE Trans. Cogn. Dev. Syst. 15(2), 887\u2013895 (2022)","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"20_CR5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2164-16-1","volume":"16","author":"SP Deng","year":"2015","unstructured":"Deng, S.P., Zhu, L., Huang, D.S.: Mining the bladder cancer-associated genes by an integrated strategy for the construction and analysis of differential co-expression networks. BMC Genomics BioMed. Central 16, 1\u201310 (2015)","journal-title":"BMC Genomics BioMed. Central"},{"issue":"7391","key":"20_CR6","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1038\/nature11003","volume":"483","author":"J Barretina","year":"2012","unstructured":"Barretina, J., Caponigro, G., Stransky, N.: The cancer cell line encyclopedia enables predictive modeling of anticancer drug sensitivity. Nature 483(7391), 603\u2013607 (2012)","journal-title":"Nature"},{"issue":"D1","key":"20_CR7","doi-asserted-by":"publisher","first-page":"D955","DOI":"10.1093\/nar\/gks1111","volume":"41","author":"W Yang","year":"2012","unstructured":"Yang, W., Soares, J., Greninger, P.: Genomics of drug sensitivity in cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 41(D1), D955\u2013D961 (2012)","journal-title":"Nucleic Acids Res."},{"issue":"22","key":"20_CR8","doi-asserted-by":"publisher","first-page":"13919","DOI":"10.3390\/ijms232213919","volume":"23","author":"J Shin","year":"2022","unstructured":"Shin, J., Piao, Y., Bang, D.: DRPreter: interpretable anticancer drug response prediction using knowledge-guided graph neural networks and transformer. Int. J. Mol. Sci. 23(22), 13919 (2022)","journal-title":"Int. J. Mol. Sci."},{"issue":"3","key":"20_CR9","doi-asserted-by":"publisher","first-page":"bbac100","DOI":"10.1093\/bib\/bbac100","volume":"23","author":"L Jiang","year":"2022","unstructured":"Jiang, L., Jiang, C., Yu, X.: DeepTTA: a transformer-based model for predicting cancer drug response. Briefings in Bioinformatics 23(3), bbac100 (2022)","journal-title":"Briefings in Bioinformatics"},{"issue":"37","key":"20_CR10","first-page":"43","volume":"35","author":"L Rampasek","year":"2019","unstructured":"Rampasek, L., Hidru, D., Smirnov, P.: Improving drug response prediction via modeling of drug perturbation effects. Brief. Bioinform. 35(37), 43\u201351 (2019)","journal-title":"Brief. Bioinform."},{"issue":"1","key":"20_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12885-017-3500-5","volume":"17","author":"L Wang","year":"2017","unstructured":"Wang, L., Li, X., Zhang, L.: Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization. BMC Cancer 17(1), 1\u201312 (2017)","journal-title":"BMC Cancer"},{"issue":"8","key":"20_CR12","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1093\/bioinformatics\/btaa921","volume":"37","author":"T Nguyen","year":"2021","unstructured":"Nguyen, T., Le, H., Quinn, T.P.: GraphDTA: predicting drug-target binding affinity with graph neural networks. Bioinformatics 37(8), 1140\u20131147 (2021)","journal-title":"Bioinformatics"},{"issue":"6","key":"20_CR13","first-page":"5640","volume":"35","author":"X Su","year":"2022","unstructured":"Su, X., You, Z., Huang, D.: Biomedical knowledge graph embedding with capsule network for multi-label drug-drug interaction prediction. IEEE Trans. Knowl. Data Eng. 35(6), 5640\u20135651 (2022)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"1","key":"20_CR14","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1109\/TCBB.2015.2476790","volume":"13","author":"SP Deng","year":"2015","unstructured":"Deng, S.P., Zhu, L., Huang, D.S.: Predicting hub genes associated with cervical cancer through gene co-expression networks. IEEE\/ACM Trans. Comput. Biol. Bioinf. 13(1), 27\u201335 (2015)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"issue":"2","key":"20_CR15","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1002375","volume":"8","author":"P Khatri","year":"2021","unstructured":"Khatri, P., Sirota, M., Butte, A.J.: Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput. Biol. 8(2), e1002375 (2021)","journal-title":"PLoS Comput. Biol."},{"key":"20_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-020-00456-1","volume":"12","author":"AP Bento","year":"2020","unstructured":"Bento, A.P., Hersey, A., F\u00e9lix, E.: An opensource chemical structure curation pipeline using RDKit. J. Cheminform. 12, 1\u201316 (2020)","journal-title":"J. Cheminform."},{"key":"20_CR17","unstructured":"Ramsundar, B., Eastman, P., Walters, P.: Deep learning for the life sciences: applying deep learning to genomics, microscopy. Drug Discov. More 1 (2019)"},{"issue":"1","key":"20_CR18","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1186\/s12859-023-05618-0","volume":"24","author":"Y Yang","year":"2023","unstructured":"Yang, Y., Li, P.: GPDRP: a multimodal framework for drug response prediction with graph transformer. BMC Bioinform. 24(1), 484 (2023)","journal-title":"BMC Bioinform."},{"key":"20_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-14-7","volume":"14","author":"S H\u00e4nzelmann","year":"2013","unstructured":"H\u00e4nzelmann, S., Castelo, R., Guinney, J.: GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinform. 14, 1\u201315 (2013)","journal-title":"BMC Bioinform."},{"issue":"2","key":"20_CR20","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1109\/TCBB.2022.3206888","volume":"20","author":"T Chu","year":"2022","unstructured":"Chu, T., Nguyen, T.T., Hai, B.D.: Graph transformer for drug response prediction. IEEE\/ACM Trans. Comput. Biol. Bioinf. 20(2), 1065\u20131072 (2022)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"issue":"7","key":"20_CR21","doi-asserted-by":"publisher","first-page":"772","DOI":"10.3390\/math9070772","volume":"9","author":"S Kim","year":"2021","unstructured":"Kim, S., Bae, S., Piao, Y.: Graph convolutional network for drug response prediction using gene expression data. Mathematics 9(7), 772 (2021)","journal-title":"Mathematics"},{"key":"20_CR22","unstructured":"Xu, K., Hu, W., Leskovec, J.: How Powerful Are Graph Neural Networks. arXiv preprint arXiv:1810.00826 (2018)"},{"key":"20_CR23","unstructured":"Yun, S., Jeong, M., Kim, R.: Graph transformer networks. Adv. Neural Inform. Process. Syst. 32 (2019)"},{"key":"20_CR24","unstructured":"Vaswani, A.: Attention is all you need. Adv. Neural Inform. Process. Syst. (2017)"},{"issue":"4","key":"20_CR25","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0061318","volume":"8","author":"MP Menden","year":"2013","unstructured":"Menden, M.P., Iorio, F., Garnett, M.: Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties. PLoS ONE 8(4), e61318 (2013)","journal-title":"PLoS ONE"},{"issue":"3","key":"20_CR26","doi-asserted-by":"publisher","first-page":"1384","DOI":"10.1109\/JBHI.2021.3102186","volume":"26","author":"W Peng","year":"2021","unstructured":"Peng, W., Chen, T., Dai, W.: Predicting drug response based on multi-omics fusion and graph convolution. IEEE J. Biomed. Health Inform. 26(3), 1384\u20131393 (2021)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"1","key":"20_CR27","doi-asserted-by":"publisher","first-page":"bbac501","DOI":"10.1093\/bib\/bbac501","volume":"24","author":"H Wang","year":"2023","unstructured":"Wang, H., Dai, C., Wen, Y.: GADRP: graph convolutional networks and autoencoders for cancer drug response prediction. Briefings Bioinform. 24(1), bbac501 (2023)","journal-title":"Briefings Bioinform."}],"container-title":["Communications in Computer and Information Science","Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-1907-8_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T19:55:14Z","timestamp":1740426914000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-1907-8_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819619061","9789819619078"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-1907-8_20","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"25 February 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Applied Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhenzhou","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icai12024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icai.org.cn\/2024\/Organization.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}