{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T22:01:55Z","timestamp":1780524115108,"version":"3.54.1"},"reference-count":60,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE\/ACM Trans. Comput. Biol. and Bioinf."],"published-print":{"date-parts":[[2022,1,1]]},"DOI":"10.1109\/tcbb.2021.3060430","type":"journal-article","created":{"date-parts":[[2021,2,20]],"date-time":"2021-02-20T06:36:40Z","timestamp":1613803000000},"page":"146-154","source":"Crossref","is-referenced-by-count":167,"title":["Graph Convolutional Networks for Drug Response Prediction"],"prefix":"10.1109","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3616-5267","authenticated-orcid":false,"given":"Tuan","family":"Nguyen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2098-568X","authenticated-orcid":false,"given":"Giang T. T.","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3467-8963","authenticated-orcid":false,"given":"Thin","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4951-5916","authenticated-orcid":false,"given":"Duc-Hau","family":"Le","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1186\/1758-2946-4-22"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2014.6958056"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-019-2910-6"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2019.2919581"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1186\/s12920-019-0569-5"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-27214-6"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/AVSS.2015.7301753"},{"key":"ref36","article-title":"Deep inside convolutional networks: Visualising image classification models and saliency maps","author":"simonyan","year":"2013"},{"key":"ref35","article-title":"Predicting drug–target binding affinity with graph neural networks","author":"nguyen","year":"2020","journal-title":"BioRxiv"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbz171"},{"key":"ref60","first-page":"63","article-title":"Systematic assessment of analytical methods for drug sensitivity prediction from cancer cell line data","author":"jang","year":"2014","journal-title":"Proc Pacific Symp Biocomputing"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1021\/acs.molpharmaceut.6b00248"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jproteome.6b00618"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz418"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gks1111"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.2764"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1080\/17460441.2016.1201262"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2017.09.007"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2019.07.006"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz111"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.6b00355"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1089\/cmb.2017.0135"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"821i","DOI":"10.1093\/bioinformatics\/bty593","article-title":"DeepDTA: deep drug–target binding affinity prediction","volume":"34","author":"\u00f6zt\u00fcrk","year":"2018","journal-title":"Bioinformatics"},{"key":"ref50","first-page":"5998","article-title":"Attention is all you need","author":"vaswani","year":"2017","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0061318"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1038\/nbt.2877"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btw449"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.3389\/fped.2017.00004"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/5907238"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1111\/j.1432-1033.1995.tb20498.x"},{"key":"ref54","author":"rosenberg","year":"2005","journal-title":"Cancer Principles & Practice of Oncology"},{"key":"ref53","article-title":"Landscape of targeted anti-cancer drug synergies in melanoma identifies a novel BRAF-VEGFR\/PDGFR combination treatment","volume":"10","year":"2015","journal-title":"PLoS ONE"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1158\/1535-7163.MCT-05-0147"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btu464"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/MAPR.2018.8337521"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkv951"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1186\/s12859-018-2060-2"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ygeno.2018.07.002"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0101183"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmb.2018.06.041"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3287921.3287974"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1004498"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1186\/s12918-017-0463-8"},{"key":"ref19","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1038\/nrc1951"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/nature11003","article-title":"The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity","volume":"483","author":"barretina","year":"2012","journal-title":"Nature"},{"key":"ref6","article-title":"A survey and systematic assessment of computational methods for drug response prediction","author":"chen","year":"2020","journal-title":"Briefings Bioinf"},{"key":"ref5","first-page":"820","article-title":"Computational models for predicting drug responses in cancer research","volume":"18","author":"azuaje","year":"2017","journal-title":"Briefings Bioinf"},{"key":"ref8","first-page":"63","article-title":"Systematic assessment of analytical methods for drug sensitivity prediction from cancer cell line data","author":"jang","year":"2014","journal-title":"Biocomputing"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1038\/nature11005"},{"key":"ref49","first-page":"3104","article-title":"Sequence to sequence learning with neural networks","author":"sutskever","year":"2014","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1038\/nbt.2877"},{"key":"ref46","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref45","first-page":"1","article-title":"How powerful are graph neural networks?","author":"xu","year":"2019","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(89)90020-8"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref42","author":"ramsundar","year":"2019","journal-title":"Deep Learning for the Life Sciences"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-020-00456-1"},{"key":"ref44","first-page":"1","article-title":"Graph attention networks","author":"veli?kovi?","year":"2018","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref43","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2017","journal-title":"Proc Int Conf Learn Representations"}],"container-title":["IEEE\/ACM Transactions on Computational Biology and Bioinformatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8857\/9702518\/09359501.pdf?arnumber=9359501","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T17:05:50Z","timestamp":1650906350000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9359501\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,1]]},"references-count":60,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tcbb.2021.3060430","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2020.04.07.030908","asserted-by":"object"}]},"ISSN":["1545-5963","1557-9964","2374-0043"],"issn-type":[{"value":"1545-5963","type":"print"},{"value":"1557-9964","type":"electronic"},{"value":"2374-0043","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,1]]}}}