{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T01:46:35Z","timestamp":1772761595948,"version":"3.50.1"},"reference-count":109,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T00:00:00Z","timestamp":1639958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072212"],"award-info":[{"award-number":["62072212"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Development Project of Jilin Province of China","award":["20200401083GX"],"award-info":[{"award-number":["20200401083GX"]}]},{"name":"Development Project of Jilin Province of China","award":["2020C003"],"award-info":[{"award-number":["2020C003"]}]},{"name":"Development Project of Jilin Province of China","award":["2020LY500L06"],"award-info":[{"award-number":["2020LY500L06"]}]},{"name":"Development Project of Jilin Province of China","award":["20200403172SF"],"award-info":[{"award-number":["20200403172SF"]}]},{"name":"Guangdong Key Project for Applied Fundamental Research","award":["2018KZDXM076"],"award-info":[{"award-number":["2018KZDXM076"]}]},{"name":"Jilin Province Key Laboratory of Big Data Intelligent Computing","award":["20180622002JC"],"award-info":[{"award-number":["20180622002JC"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Several factors, including advances in computational algorithms, the availability of high-performance computing hardware, and the assembly of large community-based databases, have led to the extensive application of Artificial Intelligence (AI) in the biomedical domain for nearly 20\u00a0years. AI algorithms have attained expert-level performance in cancer research. However, only a few AI-based applications have been approved for use in the real world. Whether AI will eventually be capable of replacing medical experts has been a hot topic. In this article, we first summarize the cancer research status using AI in the past two decades, including the consensus on the procedure of AI based on an ideal paradigm and current efforts of the expertise and domain knowledge. Next, the available data of AI process in the biomedical domain are surveyed. Then, we review the methods and applications of AI in cancer clinical research categorized by the data types including radiographic imaging, cancer genome, medical records, drug information and biomedical literatures. At last, we discuss challenges in moving AI from theoretical research to real-world cancer research applications and the perspectives toward the future realization of AI participating cancer treatment.<\/jats:p>","DOI":"10.1093\/bib\/bbab523","type":"journal-article","created":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T12:09:18Z","timestamp":1637064558000},"source":"Crossref","is-referenced-by-count":46,"title":["Artificial intelligence in clinical research of cancers"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1050-2808","authenticated-orcid":false,"given":"Dan","family":"Shao","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Key Laboratory of Human Health Status Identification and Function Enhancement of Jilin Province, Changchun University, Changchun 130022, China"}]},{"given":"Yinfei","family":"Dai","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Key Laboratory of Human Health Status Identification and Function Enhancement of Jilin Province, Changchun University, Changchun 130022, China"}]},{"given":"Nianfeng","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Key Laboratory of Human Health Status Identification and Function Enhancement of Jilin Province, Changchun University, Changchun 130022, China"}]},{"given":"Xuqing","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Neurology, People\u2019s Hospital of Ningxia Hui Autonomous Region (The Affiliated people\u2019s Hospital of Ningxia Medical University and The First Affiliated Hospital of Northwest Minzu University), Yinchuan 750002, China"}]},{"given":"Wei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, Ningxia Medical University, Yinchuan 750002, China"}]},{"given":"Li","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Electrical Diagnosis, Affiliated Hospital of Changchun University of Traditional Chinese Medicine, Changchun, 130021, China"}]},{"given":"Zhuqing","family":"Rong","sequence":"additional","affiliation":[{"name":"School of Science, Key Laboratory of Human Health Status Identification and Function Enhancement of Jilin Province, Changchun University, Changchun 130022, China"}]},{"given":"Lan","family":"Huang","sequence":"additional","affiliation":[{"name":"Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4751-0708","authenticated-orcid":false,"given":"Yan","family":"Wang","sequence":"additional","affiliation":[{"name":"Key laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China"}]},{"given":"Jing","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, 43210, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,12,21]]},"reference":[{"key":"2022012000330773200_ref1","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.trecan.2019.02.002","article-title":"Rise of the machines: advances in deep learning for cancer diagnosis","volume":"5","author":"Levine","year":"2019","journal-title":"Trends Cancer"},{"key":"2022012000330773200_ref2","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1038\/s41573-019-0024-5","article-title":"Applications of machine learning in drug discovery and development","volume":"18","author":"Vamathevan","year":"2019","journal-title":"Nat Rev Drug Discov"},{"key":"2022012000330773200_ref3","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1613\/jair.1.11222","article-title":"When will AI exceed human performance? Evidence from AI experts","volume":"62","author":"Grace","year":"2018","journal-title":"J Artif Intell Res"},{"issue":"3 Suppl","key":"2022012000330773200_ref4","first-page":"S75","article-title":"Ensemble machine learning on gene expression data for cancer classification","volume":"2","author":"Tan","year":"2003","journal-title":"Appl Bioinformatics"},{"key":"2022012000330773200_ref5","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv Neural Inform Process Syst"},{"key":"2022012000330773200_ref6","first-page":"1","article-title":"Exploring strategies for training deep neural networks","volume":"10","author":"Larochelle","year":"2009","journal-title":"J Mach Learn Res"},{"key":"2022012000330773200_ref7","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.patcog.2017.10.013","article-title":"Recent advances in convolutional neural networks","volume":"77","author":"Gu","year":"2018","journal-title":"Pattern Recognition"},{"key":"2022012000330773200_ref8","doi-asserted-by":"crossref","DOI":"10.1201\/9781420049176","volume-title":"Recurrent neural networks: design and applications","author":"Medsker","year":"1999"},{"key":"2022012000330773200_ref9","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1038\/nature07385","article-title":"Comprehensive genomic characterization defines human glioblastoma genes and core pathways","volume":"455","author":"Cancer Genome Atlas Research Network","year":"2008","journal-title":"Nature"},{"key":"2022012000330773200_ref10","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/MSPEC.2019.8678513","article-title":"IBM Watson, heal thyself: how IBM overpromised and underdelivered on AI health care","volume":"56","author":"Strickland","year":"2019","journal-title":"IEEE Spectrum"},{"key":"2022012000330773200_ref11","article-title":"How Microsoft computer scientists and researchers are working to \u2018solve\u2019 cancer [Internet]","author":"Linn","year":"2028"},{"key":"2022012000330773200_ref12","first-page":"1","volume-title":"2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)","author":"Singireddy","year":"2015"},{"key":"2022012000330773200_ref13","doi-asserted-by":"crossref","first-page":"155","DOI":"10.3390\/genes9030155","article-title":"Identification of differentially expressed genes between original breast cancer and xenograft using machine learning algorithms","volume":"9","author":"Wang","year":"2018","journal-title":"Genes"},{"key":"2022012000330773200_ref14","first-page":"351","volume-title":"In Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","author":"Zhang","year":"2018"},{"key":"2022012000330773200_ref15","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1038\/s41467-020-20603-4","article-title":"Sarcoma classification by DNA methylation profiling","volume":"12","author":"Koelsche","year":"2021","journal-title":"Nat Commun"},{"key":"2022012000330773200_ref16","first-page":"e0189302","article-title":"Diffusion-weighted imaging features of breast tumours and the surrounding stroma reflect intrinsic heterogeneous characteristics of molecular subtypes in breast cancer","volume":"31","author":"Ming","year":"2018","journal-title":"NMR Biomed"},{"key":"2022012000330773200_ref17","doi-asserted-by":"crossref","first-page":"46732","DOI":"10.1038\/srep46732","article-title":"Quantitative diagnosis of breast tumors by morphometric classification of microenvironmental myoepithelial cells using a machine learning approach","volume":"7","author":"Yamamoto","year":"2017","journal-title":"Sci Rep"},{"key":"2022012000330773200_ref18","doi-asserted-by":"crossref","first-page":"11182","DOI":"10.1021\/acsnano.7b05503","article-title":"Combining machine learning and nanofluidic technology to diagnose pancreatic cancer using exosomes","volume":"11","author":"Ko","year":"2017","journal-title":"ACS Nano"},{"key":"2022012000330773200_ref19","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1186\/s12859-016-1334-9","article-title":"DeepGene: an advanced cancer type classifier based on deep learning and somatic point mutations","volume":"17","author":"Yuan","year":"2016","journal-title":"BMC Bioinformatics"},{"key":"2022012000330773200_ref20","doi-asserted-by":"crossref","first-page":"17946","DOI":"10.1038\/s41598-017-17858-1","article-title":"Machine learning for nuclear mechano-morphometric biomarkers in cancer diagnosis","volume":"7","author":"Radhakrishnan","year":"2017","journal-title":"Sci Rep"},{"key":"2022012000330773200_ref21","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1038\/s41591-019-0715-9","article-title":"Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks","volume":"26","author":"Hollon","year":"2020","journal-title":"Nat Med"},{"key":"2022012000330773200_ref22","volume-title":"International Conference on Computational Science and Computational Intelligence (CSCI)","author":"Guillen","year":"2016"},{"key":"2022012000330773200_ref23","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1038\/s41523-018-0079-1","article-title":"Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype","volume":"4","author":"Couture","year":"2018","journal-title":"NPJ Breast Cancer"},{"key":"2022012000330773200_ref24","doi-asserted-by":"crossref","first-page":"1632","DOI":"10.1109\/JBHI.2019.2956351","article-title":"Joint prediction of breast cancer histological grade and Ki-67 expression level based on DCE-MRI and DWI radiomics","volume":"24","author":"Fan","year":"2019","journal-title":"IEEE J Biomed Health Inform"},{"key":"2022012000330773200_ref25","doi-asserted-by":"crossref","first-page":"215001","DOI":"10.1088\/1361-6560\/ab3fd3","article-title":"Integration of dynamic contrast-enhanced magnetic resonance imaging and T2-weighted imaging radiomic features by a canonical correlation analysis-based feature fusion method to predict histological grade in ductal breast carcinoma","volume":"64","author":"Fan","year":"2019","journal-title":"Phys Med Biol"},{"key":"2022012000330773200_ref26","doi-asserted-by":"crossref","first-page":"5301","DOI":"10.1038\/s41598-017-05728-9","article-title":"Deep learning for fully-automated localization and segmentation of rectal cancer on multiparametric MR","volume":"7","author":"Trebeschi","year":"2017","journal-title":"Sci Rep"},{"key":"2022012000330773200_ref27","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.lungcan.2018.11.001","article-title":"Automated detection of lung cancer at ultralow dose PET\/CT by deep neural networks\u2013initial results","volume":"126","author":"Schwyzer","year":"2018","journal-title":"Lung Cancer"},{"key":"2022012000330773200_ref28","doi-asserted-by":"crossref","first-page":"041304","DOI":"10.1117\/1.JMI.4.4.041304","article-title":"Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms","volume":"4","author":"Li","year":"2017","journal-title":"J Med Imaging"},{"key":"2022012000330773200_ref29","doi-asserted-by":"crossref","first-page":"13149","DOI":"10.1038\/s41598-018-31573-5","article-title":"Machine learning identifies interacting genetic variants contributing to breast cancer risk: a case study in Finnish cases and controls","volume":"8","author":"Behravan","year":"2018","journal-title":"Sci Rep"},{"key":"2022012000330773200_ref30","doi-asserted-by":"crossref","first-page":"1570","DOI":"10.1038\/s41598-018-38381-x","article-title":"Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images","volume":"9","author":"Varghese","year":"2019","journal-title":"Sci Rep"},{"key":"2022012000330773200_ref31","doi-asserted-by":"crossref","first-page":"1861","DOI":"10.1038\/s41598-020-58821-x","article-title":"RefDNN: a reference drug based neural network for more accurate prediction of anticancer drug resistance","volume":"10","author":"Choi","year":"2020","journal-title":"Sci Rep"},{"key":"2022012000330773200_ref32","doi-asserted-by":"crossref","first-page":"8857","DOI":"10.1038\/s41598-018-27214-6","article-title":"Cancer drug response profile scan (CDRscan): a deep learning model that predicts drug effectiveness from cancer genomic signature","volume":"8","author":"Chang","year":"2018","journal-title":"Sci Rep"},{"key":"2022012000330773200_ref33","doi-asserted-by":"crossref","first-page":"13190","DOI":"10.1038\/s41598-017-13196-4","article-title":"A novel machine learning approach reveals latent vascular phenotypes predictive of renal cancer outcome","volume":"7","author":"Ing","year":"2017","journal-title":"Sci Rep"},{"key":"2022012000330773200_ref34","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1002\/jmri.25921","article-title":"DCE-MRI texture analysis with tumor subregion partitioning for predicting Ki-67 status of estrogen receptor-positive breast cancers","volume":"48","author":"Fan","year":"2018","journal-title":"J Magn Reson Imaging"},{"key":"2022012000330773200_ref35","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1007\/s00521-013-1359-1","article-title":"Application of machine learning to predict the recurrence-proneness for cervical cancer","volume":"24","author":"Tseng","year":"2014","journal-title":"Neural Comput Applic"},{"key":"2022012000330773200_ref36","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.radonc.2018.10.019","article-title":"Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer","volume":"132","author":"Wang","year":"2019","journal-title":"Radiother Oncol"},{"key":"2022012000330773200_ref37","doi-asserted-by":"crossref","first-page":"8738","DOI":"10.1038\/s41598-017-09315-w","article-title":"Bladder cancer treatment response assessment in CT using radiomics with deep-learning","volume":"7","author":"Cha","year":"2017","journal-title":"Sci Rep"},{"key":"2022012000330773200_ref38","doi-asserted-by":"crossref","first-page":"3266","DOI":"10.1158\/1078-0432.CCR-18-2495","article-title":"Deep learning predicts lung cancer treatment response from serial medical imaging","volume":"25","author":"Xu","year":"2019","journal-title":"Clin Cancer Res"},{"key":"2022012000330773200_ref39","doi-asserted-by":"crossref","first-page":"12611","DOI":"10.1038\/s41598-018-30657-6","article-title":"Deep learning and radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer","volume":"8","author":"Bibault","year":"2018","journal-title":"Sci Rep"},{"key":"2022012000330773200_ref40","doi-asserted-by":"crossref","first-page":"622219","DOI":"10.3389\/fmolb.2021.622219","article-title":"Radiomics of tumor heterogeneity in longitudinal dynamic contrast-enhanced magnetic resonance imaging for predicting response to neoadjuvant chemotherapy in breast cancer","volume":"8","author":"Fan","year":"2021","journal-title":"Front Mol Biosci"},{"key":"2022012000330773200_ref41","doi-asserted-by":"crossref","first-page":"14036","DOI":"10.1038\/s41598-018-32441-y","article-title":"Pretreatment identification of head and neck cancer nodal metastasis and extranodal extension using deep learning neural networks","volume":"8","author":"Kann","year":"2018","journal-title":"Sci Rep"},{"key":"2022012000330773200_ref42","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1038\/s41746-019-0112-2","article-title":"Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer","volume":"2","author":"Nagpal","year":"2019","journal-title":"NPJ Digit Med"},{"key":"2022012000330773200_ref43","doi-asserted-by":"crossref","DOI":"10.17226\/27111","volume-title":"Artificial Intelligence in Health Care: The Hope, The Hype, The Promise, The Peril","author":"Matheny","year":"2019"},{"key":"2022012000330773200_ref44","doi-asserted-by":"crossref","first-page":"10353","DOI":"10.1038\/s41598-017-10649-8","article-title":"A deep learning-based radiomics model for prediction of survival in glioblastoma multiforme","volume":"7","author":"Lao","year":"2017","journal-title":"Sci Rep"},{"key":"2022012000330773200_ref45","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1186\/s13058-019-1199-8","article-title":"Tumour heterogeneity revealed by unsupervised decomposition of dynamic contrast-enhanced magnetic resonance imaging is associated with underlying gene expression patterns and poor survival in breast cancer patients","volume":"21","author":"Fan","year":"2019","journal-title":"Breast Cancer Res"},{"key":"2022012000330773200_ref46","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1186\/1471-2105-14-107","article-title":"Non-negative matrix factorization by maximizing correntropy for cancer clustering","volume":"14","author":"Wang","year":"2013","journal-title":"BMC Bioinformatics"},{"key":"2022012000330773200_ref47","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1093\/bib\/bbz160","article-title":"Human body-fluid proteome: quantitative profiling and computational prediction","volume":"22","author":"Huang","year":"2021","journal-title":"Brief Bioinform"},{"key":"2022012000330773200_ref48","doi-asserted-by":"crossref","first-page":"10393","DOI":"10.1038\/s41598-018-27707-4","article-title":"Comprehensive analysis of lung cancer pathology images to discover tumor shape and boundary features that predict survival outcome","volume":"8","author":"Wang","year":"2018","journal-title":"Sci Rep"},{"key":"2022012000330773200_ref49","doi-asserted-by":"crossref","first-page":"e4345","DOI":"10.1002\/nbm.4345","article-title":"Generative adversarial network-based super-resolution of diffusion-weighted imaging: application to tumour radiomics in breast cancer","volume":"33","author":"Fan","year":"2020","journal-title":"NMR Biomed"},{"key":"2022012000330773200_ref50","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1038\/s41598-018-37741-x","article-title":"A feasibility study for predicting optimal radiation therapy dose distributions of prostate cancer patients from patient anatomy using deep learning","volume":"9","author":"Nguyen","year":"2019","journal-title":"Sci Rep"},{"key":"2022012000330773200_ref51","doi-asserted-by":"crossref","first-page":"E2970","DOI":"10.1073\/pnas.1717139115","article-title":"Predicting cancer outcomes from histology and genomics using convolutional networks","volume":"15","author":"Mobadersany","year":"2018","journal-title":"Proc Natl Acad Sci USA"},{"key":"2022012000330773200_ref52","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"2022012000330773200_ref53","doi-asserted-by":"crossref","first-page":"1581","DOI":"10.1016\/j.cell.2018.05.015","article-title":"Next-generation machine learning for biological networks","volume":"173","author":"Camacho","year":"2018","journal-title":"Cell"},{"key":"2022012000330773200_ref54","author":"Rhee"},{"key":"2022012000330773200_ref55","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Li","year":"2018"},{"key":"2022012000330773200_ref56","doi-asserted-by":"crossref","first-page":"111","DOI":"10.3390\/cancers11010111","article-title":"A review on a deep learning perspective in brain cancer classification","volume":"11","author":"Tandel","year":"2019","journal-title":"Cancers (Basel)"},{"key":"2022012000330773200_ref57","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.ygeno.2017.01.004","article-title":"Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts","volume":"109","author":"Dashtban","year":"2017","journal-title":"Genomics"},{"key":"2022012000330773200_ref58","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1038\/89044","article-title":"Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks","volume":"7","author":"Khan","year":"2001","journal-title":"Nat Med"},{"key":"2022012000330773200_ref59","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1038\/35000501","article-title":"Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling","volume":"403","author":"Alizadeh","year":"2000","journal-title":"Nature"},{"key":"2022012000330773200_ref60","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1016\/j.cell.2018.12.021","article-title":"The landscape of circular RNA in cancer","volume":"176","author":"Vo","year":"2019","journal-title":"Cell"},{"key":"2022012000330773200_ref61","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.semcancer.2019.09.023","article-title":"Computer-aided drug repurposing for cancer therapy: approaches and opportunities to challenge anticancer targets","volume":"68","author":"Mottini","year":"2021","journal-title":"Semin Cancer Biol"},{"key":"2022012000330773200_ref62","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1016\/j.cell.2017.06.010","article-title":"Defining a cancer dependency map","volume":"170","author":"Tsherniak","year":"2017","journal-title":"Cell"},{"key":"2022012000330773200_ref63","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1016\/j.cell.2016.06.017","article-title":"A landscape of pharmacogenomics interactions in cancer","volume":"166","author":"Iorio","year":"2016","journal-title":"Cell"},{"key":"2022012000330773200_ref64","doi-asserted-by":"crossref","first-page":"D917","DOI":"10.1093\/nar\/gky1129","article-title":"canSAR: update to the cancer translational research and drug discovery knowledgebase","volume":"47","author":"Coker","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2022012000330773200_ref65","doi-asserted-by":"crossref","first-page":"D985","DOI":"10.1093\/nar\/gkw1055","article-title":"Open targets: a platform for therapeutic target identification and validation","volume":"45","author":"Koscielny","year":"2017","journal-title":"Nucleic Acids Res"},{"key":"2022012000330773200_ref66","doi-asserted-by":"crossref","first-page":"D921","DOI":"10.1093\/nar\/gku955","article-title":"Open TG-GATEs: a large-scale toxicogenomics database","volume":"43","author":"Igarashi","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2022012000330773200_ref67","doi-asserted-by":"crossref","first-page":"D1074","DOI":"10.1093\/nar\/gkx1037","article-title":"DrugBank 5.0: a major update to the DrugBank database for 2018","volume":"46","author":"Wishart","year":"2018","journal-title":"Nucleic Acids Res"},{"key":"2022012000330773200_ref68","doi-asserted-by":"crossref","first-page":"11979","DOI":"10.1038\/s41598-017-12320-8","article-title":"Automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning","volume":"7","author":"Aubreville","year":"2017","journal-title":"Sci Rep"},{"key":"2022012000330773200_ref69","doi-asserted-by":"crossref","first-page":"619","DOI":"10.5858\/arpa.2016-0471-ED","article-title":"Alphago, deep learning, and the future of the human microscopist","volume":"141","author":"Granter","year":"2017","journal-title":"Arch Pathol Lab Med"},{"key":"2022012000330773200_ref70","author":"Vang"},{"key":"2022012000330773200_ref71","doi-asserted-by":"crossref","first-page":"15415","DOI":"10.1038\/s41598-017-15720-y","article-title":"Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning","volume":"7","author":"Wang","year":"2017","journal-title":"Sci Rep"},{"key":"2022012000330773200_ref72","doi-asserted-by":"crossref","first-page":"4172","DOI":"10.1038\/s41598-017-04075-z","article-title":"Breast cancer multi-classification from histopathological images with structured deep learning model","volume":"7","author":"Han","year":"2017","journal-title":"Sci Rep"},{"key":"2022012000330773200_ref73","doi-asserted-by":"crossref","first-page":"4456","DOI":"10.1007\/s00330-018-5891-3","article-title":"Radiomic analysis of imaging heterogeneity in tumours and the surrounding parenchyma based on unsupervised decomposition of DCE-MRI for predicting molecular subtypes of breast cancer","volume":"29","author":"Fan","year":"2019","journal-title":"Eur Radiol"},{"key":"2022012000330773200_ref74","article-title":"High-resolution breast cancer screening with multi-view deep convolutional neural networks","author":"Geras","year":"2017","journal-title":"arXiv"},{"key":"2022012000330773200_ref75","doi-asserted-by":"crossref","first-page":"6600","DOI":"10.1038\/s41598-018-25005-7","article-title":"An improved deep learning approach for detection of thyroid papillary cancer in ultrasound images","volume":"8","author":"Li","year":"2018","journal-title":"Sci Rep"},{"key":"2022012000330773200_ref76","doi-asserted-by":"crossref","first-page":"599333","DOI":"10.3389\/fmolb.2020.599333","article-title":"Mass detection and segmentation in digital breast tomosynthesis using 3D-mask region-based convolutional neural network: a comparative analysis","volume":"7","author":"Fan","year":"2020","journal-title":"Front Mol Biosci"},{"key":"2022012000330773200_ref77","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1109\/RBME.2009.2034865","article-title":"Histopathological image analysis: a review","volume":"2","author":"Gurcan","year":"2009","journal-title":"IEEE Rev Biomed Eng"},{"key":"2022012000330773200_ref78","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1038\/s41576-019-0122-6","article-title":"Deep learning: new computational modelling techniques for genomics","volume":"20","author":"Eraslan","year":"2019","journal-title":"Nat Rev Genet"},{"key":"2022012000330773200_ref79","doi-asserted-by":"crossref","first-page":"a001578","DOI":"10.1101\/mcs.a001578","article-title":"Personalized cancer therapy-leveraging a knowledge base for clinical decision-making","volume":"4","author":"Dumbrava","year":"2018","journal-title":"Cold Spring Harb Mol Case Stud"},{"key":"2022012000330773200_ref80","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1038\/s12276-020-0422-0","article-title":"Single-cell transcriptomics in cancer: computational challenges and opportunities","volume":"52","author":"Fan","year":"2020","journal-title":"Exp Mol Med"},{"key":"2022012000330773200_ref81","first-page":"btab545","article-title":"DeepSec: a deep learning framework for secreted protein discovery in human body fluids","volume":"2021","author":"Shao","year":"2021","journal-title":"Bioinformatics"},{"key":"2022012000330773200_ref82","doi-asserted-by":"crossref","first-page":"144168","DOI":"10.1016\/j.gene.2019.144168","article-title":"Analysis of the microarray gene expression for breast cancer progression after the application modified logistic regression","volume":"726","author":"Morais-Rodrigues","year":"2020","journal-title":"Gene"},{"key":"2022012000330773200_ref83","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1038\/s41596-019-0251-6","article-title":"Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data","volume":"15","author":"Maros","year":"2020","journal-title":"Nat Protoc"},{"key":"2022012000330773200_ref84","doi-asserted-by":"crossref","first-page":"4404","DOI":"10.1016\/j.csbj.2021.08.006","article-title":"MetaCancer: a deep learning-based pan-cancer metastasis prediction model developed using multi-omics data","volume":"19","author":"Albaradei","year":"2021","journal-title":"Comput Struct Biotechnol J"},{"key":"2022012000330773200_ref85","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1093\/bib\/bbz080","article-title":"Predicting disease-associated circular RNAs using deep forests combined with positive-unlabeled learning methods","volume":"21","author":"Zeng","year":"2020","journal-title":"Brief Bioinform"},{"key":"2022012000330773200_ref86","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1016\/j.ccell.2021.04.002","article-title":"Artificial intelligence for clinical oncology","volume":"39","author":"Kann","year":"2021","journal-title":"Cancer Cell"},{"key":"2022012000330773200_ref87","first-page":"565","article-title":"Facilitating cancer research using natural language processing of pathology reports","volume":"107","author":"Xu","year":"2004","journal-title":"Stud Health Technol Inform"},{"key":"2022012000330773200_ref88","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1200\/CCI.20.00165","article-title":"Development and use of natural language processing for identification of distant cancer recurrence and sites of distant recurrence using unstructured electronic health record data","volume":"5","author":"Karimi","year":"2021","journal-title":"JCO Clin Cancer Info"},{"key":"2022012000330773200_ref89","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1200\/CCI.20.00173","article-title":"Natural language processing to identify cancer treatments with electronic medical records","volume":"5","author":"Zeng","year":"2021","journal-title":"JCO Clin Cancer Info"},{"key":"2022012000330773200_ref90","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1016\/j.tips.2019.06.004","article-title":"Advancing drug discovery via artificial intelligence","volume":"40","author":"Chan","year":"2019","journal-title":"Trends Pharmacol Sci"},{"key":"2022012000330773200_ref91","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1080\/17460441.2019.1637414","article-title":"Transforming cancer drug discovery with big data and AI","volume":"14","author":"Workman","year":"2019","journal-title":"Expert Opin Drug Discovery"},{"key":"2022012000330773200_ref92","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.drudis.2020.10.010","article-title":"Artificial intelligence in drug discovery and development","volume":"26","author":"Paul","year":"2021","journal-title":"Drug Discov Today"},{"key":"2022012000330773200_ref93","doi-asserted-by":"crossref","first-page":"16444","DOI":"10.1038\/s41598-018-34753-5","article-title":"Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy","volume":"8","author":"Huang","year":"2018","journal-title":"Sci Rep"},{"key":"2022012000330773200_ref94","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1080\/15384101.2017.1417706","article-title":"A method of gene expression data transfer from cell lines to cancer patients for machine-learning prediction of drug efficiency","volume":"17","author":"Borisov","year":"2018","journal-title":"Cell Cycle"},{"key":"2022012000330773200_ref95","doi-asserted-by":"crossref","first-page":"3166","DOI":"10.3390\/ijms21093166","article-title":"Convolutional neural network can recognize drug resistance of single cancer cells","volume":"21","author":"Yanagisawa","year":"2020","journal-title":"Int J Mol Sci"},{"key":"2022012000330773200_ref96","volume-title":"Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery and More","author":"Ramsundar","year":"2019"},{"key":"2022012000330773200_ref97","doi-asserted-by":"crossref","first-page":"80","DOI":"10.3389\/fenvs.2015.00080","article-title":"DeepTox: toxicity prediction using deep learning","volume":"3","author":"Mayr","year":"2016","journal-title":"Front Environ Sci"},{"key":"2022012000330773200_ref98","doi-asserted-by":"crossref","first-page":"1498","DOI":"10.1093\/bioinformatics\/btx800","article-title":"gene2drug: a computational tool for pathway-based rational drug repositioning","volume":"34","author":"Napolitano","year":"2017","journal-title":"Bioinformatics"},{"key":"2022012000330773200_ref99","doi-asserted-by":"crossref","first-page":"D401","DOI":"10.1093\/nar\/gkt1207","article-title":"STITCH 4: integration of protein\u2013chemical interactions with user data","volume":"42","author":"Kuhn","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2022012000330773200_ref100","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1038\/s41586-019-1923-7","article-title":"Improved protein structure prediction using potentials from deep learning","volume":"577","author":"Senior","year":"2020","journal-title":"Nature"},{"key":"2022012000330773200_ref101","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1148\/radiol.10091808","article-title":"The national lung screening trial: overview and study design","volume":"258","author":"Aberle","year":"2011","journal-title":"Radiology"},{"key":"2022012000330773200_ref102","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1182\/blood-2017-03-735654","article-title":"The NCI genomic data commons as an engine for precision medicine","volume":"130","author":"Jensen","year":"2017","journal-title":"Blood"},{"key":"2022012000330773200_ref103","doi-asserted-by":"crossref","first-page":"e123","DOI":"10.1158\/0008-5472.CAN-17-0341","article-title":"\"Personalized cancer therapy\": a publicly available precision oncology resource","volume":"77","author":"Kurnit","year":"2017","journal-title":"Cancer Res"},{"key":"2022012000330773200_ref104","doi-asserted-by":"crossref","first-page":"D1090","DOI":"10.1093\/nar\/gky1042","article-title":"PreMedKB: an integrated precision medicine knowledgebase for interpreting relationships between diseases, genes, variants and drugs","volume":"47","author":"Yu","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2022012000330773200_ref105","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1038\/ng.3774","article-title":"CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer","volume":"49","author":"Griffith","year":"2017","journal-title":"Nat Genet"},{"key":"2022012000330773200_ref106","doi-asserted-by":"crossref","first-page":"baab065","DOI":"10.1093\/database\/baab065","article-title":"HBFP: a new repository for human body fluid proteome","volume":"2021","author":"Shao","year":"2021","journal-title":"Database"},{"key":"2022012000330773200_ref107","doi-asserted-by":"crossref","first-page":"i446","DOI":"10.1093\/bioinformatics\/btz342","article-title":"Deep learning with multimodal representation for pancancer prognosis prediction","volume":"35","author":"Cheerla","year":"2019","journal-title":"Bioinformatics"},{"key":"2022012000330773200_ref108","doi-asserted-by":"crossref","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","article-title":"Peeking inside the black-box: a survey on explainable artificial intelligence (XAI)","volume":"6","author":"Adadi","year":"2018","journal-title":"IEEE Access"},{"key":"2022012000330773200_ref109","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1186\/s13059-020-02100-5","article-title":"Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data","volume":"21","author":"Fortelny","year":"2020","journal-title":"Genome Biol"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/1\/bbab523\/42231884\/bbab523.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/23\/1\/bbab523\/42231884\/bbab523.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T08:15:00Z","timestamp":1726128900000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbab523\/6470966"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,21]]},"references-count":109,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1,17]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbab523","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,1]]},"published":{"date-parts":[[2021,12,21]]},"article-number":"bbab523"}}