{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T13:27:41Z","timestamp":1786368461793,"version":"build-2736575974"},"reference-count":94,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T00:00:00Z","timestamp":1680739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Genet."],"abstract":"<jats:p>Late-stage drug development failures are usually a consequence of ineffective targets. Thus, proper target identification is needed, which may be possible using computational approaches. The reason being, effective targets have disease-relevant biological functions, and omics data unveil the proteins involved in these functions. Also, properties that favor the existence of binding between drug and target are deducible from the protein\u2019s amino acid sequence. In this work, we developed OncoRTT, a deep learning (DL)-based method for predicting novel therapeutic targets. OncoRTT is designed to reduce suboptimal target selection by identifying novel targets based on features of known effective targets using DL approaches. First, we created the \u201cOncologyTT\u201d datasets, which include genes\/proteins associated with ten prevalent cancer types. Then, we generated three sets of features for all genes: omics features, the proteins\u2019 amino-acid sequence BERT embeddings, and the integrated features to train and test the DL classifiers separately. The models achieved high prediction performances in terms of area under the curve (AUC), i.e., AUC greater than 0.88 for all cancer types, with a maximum of 0.95 for leukemia. Also, OncoRTT outperformed the state-of-the-art method using their data in five out of seven cancer types commonly assessed by both methods. Furthermore, OncoRTT predicts novel therapeutic targets using new test data related to the seven cancer types. We further corroborated these results with other validation evidence using the Open Targets Platform and a case study focused on the top-10 predicted therapeutic targets for lung cancer.<\/jats:p>","DOI":"10.3389\/fgene.2023.1139626","type":"journal-article","created":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T05:04:01Z","timestamp":1680757441000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["OncoRTT: Predicting novel oncology-related therapeutic targets using BERT embeddings and omics features"],"prefix":"10.3389","volume":"14","author":[{"given":"Maha A.","family":"Thafar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Somayah","family":"Albaradei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahmut","family":"Uludag","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mona","family":"Alshahrani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takashi","family":"Gojobori","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Magbubah","family":"Essack","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,4,6]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"913602","DOI":"10.3389\/fmolb.2022.913602","article-title":"MetastaSite: Predicting metastasis to different sites using deep learning with gene expression data","volume":"9","author":"Albaradei","year":"2022","journal-title":"Front. Mol. Biosci."},{"key":"B2","doi-asserted-by":"publisher","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":"","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"B3","doi-asserted-by":"publisher","first-page":"5008","DOI":"10.1016\/j.csbj.2021.09.001","article-title":"Machine learning and deep learning methods that use omics data for metastasis prediction","volume":"19","author":"Albaradei","year":"","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"B4","doi-asserted-by":"crossref","DOI":"10.1145\/3383783.3383792","article-title":"Metastatic state of colorectal cancer can be accurately predicted with methylome","author":"Albaradei","year":"2019"},{"key":"B5","doi-asserted-by":"publisher","first-page":"771092","DOI":"10.3389\/fgene.2021.771092","article-title":"Predicting bone metastasis using gene expression-based machine learning models","volume":"12","author":"Albaradei","year":"","journal-title":"Front. Genet."},{"key":"B6","doi-asserted-by":"publisher","first-page":"104821","DOI":"10.1016\/j.compbiomed.2021.104821","article-title":"TRP-BERT: Discrimination of transient receptor potential (TRP) channels using contextual representations from deep bidirectional transformer based on BERT","volume":"137","author":"Ali Shah","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"B7","doi-asserted-by":"publisher","first-page":"104259","DOI":"10.1016\/j.compbiomed.2021.104259","article-title":"GT-Finder: Classify the family of glucose transporters with pre-trained BERT language models","volume":"131","author":"Ali Shah","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"B8","doi-asserted-by":"publisher","first-page":"e13061","DOI":"10.7717\/peerj.13061","article-title":"Combining biomedical knowledge graphs and text to improve predictions for drug-target interactions and drug-indications","volume":"10","author":"Alshahrani","year":"2022","journal-title":"PeerJ"},{"key":"B9","doi-asserted-by":"publisher","first-page":"e341","DOI":"10.7717\/peerj-cs.341","article-title":"Application and evaluation of knowledge graph embeddings in biomedical data","volume":"7","author":"Alshahrani","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"B10","doi-asserted-by":"publisher","first-page":"17405","DOI":"10.1038\/s41598-019-53454-1","article-title":"Ontology-based prediction of cancer driver genes","volume":"9","author":"Althubaiti","year":"2019","journal-title":"Sci. Rep."},{"key":"B11","volume-title":"DeepMOCCA: A pan-cancer prognostic model identifies personalized prognostic markers through graph attention and multi-omics data integration","author":"Althubaiti","year":"2021"},{"key":"B12","first-page":"1","article-title":"A two-stage classifier for predicting protein-ATP binding sites from protein sequence","volume-title":"IEEE conference on computational intelligence in Bioinformatics and computational biology (CIBCB), 2015","author":"Andrews","year":"2015"},{"key":"B13","doi-asserted-by":"publisher","first-page":"104458","DOI":"10.1016\/j.chemolab.2021.104458","article-title":"StackACPred: Prediction of anticancer peptides by integrating optimized multiple feature descriptors with stacked ensemble approach","volume":"220","author":"Arif","year":"2022","journal-title":"Chemom. Intelligent Laboratory Syst."},{"key":"B14","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1093\/bioinformatics\/btp002","article-title":"Properties and identification of human protein drug targets","volume":"25","author":"Bakheet","year":"2009","journal-title":"Bioinformatics"},{"key":"B15","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1038\/sj.bjc.6601894","article-title":"The COSMIC (catalogue of somatic mutations in cancer) database and website","volume":"91","author":"Bamford","year":"2004","journal-title":"Br. J. Cancer"},{"key":"B16","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1158\/0008-5472.CAN-18-1995","article-title":"Mouse models of overexpression reveal distinct oncogenic roles for different type I protein arginine methyltransferases","volume":"79","author":"Bao","year":"2019","journal-title":"Cancer Res."},{"key":"B17","doi-asserted-by":"publisher","first-page":"10787","DOI":"10.1038\/s41598-020-67846-1","article-title":"Genome-wide investigation of gene-cancer associations for the prediction of novel therapeutic targets in oncology","volume":"10","author":"Bazaga","year":"2020","journal-title":"Sci. Rep."},{"key":"B18","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1007\/s10994-020-05877-5","article-title":"Learning from positive and unlabeled data: A survey","volume":"109","author":"Bekker","year":"2020","journal-title":"Mach. Learn."},{"key":"B19","doi-asserted-by":"publisher","first-page":"380","DOI":"10.20959\/wjpr20175-8324","article-title":"The cost of new drug delivery and development \u2013 a review","author":"Bhavana","year":"2017","journal-title":"World J. Pharm. Res."},{"key":"B20","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1053\/j.ackd.2016.11.012","article-title":"Autosomal dominant tubulointerstitial kidney disease","volume":"24","author":"Bleyer","year":"2017","journal-title":"Adv. Chronic Kidney Dis."},{"key":"B21","doi-asserted-by":"publisher","first-page":"e0117955","DOI":"10.1371\/journal.pone.0117955","article-title":"Properties of protein drug target classes","volume":"10","author":"Bull","year":"2015","journal-title":"PloS one"},{"key":"B22","doi-asserted-by":"publisher","first-page":"2556","DOI":"10.1093\/bioinformatics\/btab133","article-title":"BERT4Bitter: A bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides","volume":"37","author":"Charoenkwan","year":"2021","journal-title":"Bioinformatics"},{"key":"B23","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.trecan.2019.02.003","article-title":"Polytherapy and targeted cancer drug resistance","volume":"5","author":"Chatterjee","year":"2019","journal-title":"Trends Cancer Res."},{"key":"B24","first-page":"1","article-title":"Package \u2018xgboost\u2019","volume":"90","author":"Chen","year":"2019"},{"key":"B25","doi-asserted-by":"publisher","first-page":"D127","DOI":"10.1093\/nar\/gkz757","article-title":"miRDB: an online database for prediction of functional microRNA targets","volume":"48","author":"Chen","year":"2020","journal-title":"Nucleic Acids Res."},{"key":"B26","doi-asserted-by":"publisher","first-page":"20170387","DOI":"10.1098\/rsif.2017.0387","article-title":"Opportunities and obstacles for deep learning in biology and medicine","volume":"15","author":"Ching","year":"2018","journal-title":"J. R. Soc. Interface"},{"key":"B27","first-page":"022","volume-title":"Keras: The Python deep learning library","author":"Chollet","year":"2018"},{"key":"B28","doi-asserted-by":"publisher","first-page":"2477","DOI":"10.1056\/NEJMra043172","article-title":"Renal-cell carcinoma","volume":"353","author":"Cohen","year":"2005","journal-title":"N. Engl. J. Med."},{"key":"B29","doi-asserted-by":"publisher","first-page":"e71","DOI":"10.1093\/nar\/gkv1507","article-title":"TCGAbiolinks: An R\/bioconductor package for integrative analysis of TCGA data","volume":"44","author":"Colaprico","year":"2016","journal-title":"Nucleic Acids Res."},{"key":"B30","doi-asserted-by":"publisher","first-page":"e113","DOI":"10.1002\/cpz1.113","article-title":"Learned embeddings from deep learning to visualize and predict protein sets","volume":"1","author":"Dallago","year":"2021","journal-title":"Curr. Protoc."},{"key":"B31","doi-asserted-by":"crossref","DOI":"10.1145\/1143844.1143874","article-title":"The relationship between precision-recall and ROC curves","author":"Davis","year":"2006"},{"key":"B32","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1186\/s12902-020-0540-7","article-title":"Plasma adiponectin, visfatin, leptin, and resistin levels and the onset of colonic polyps in patients with prediabetes","volume":"20","author":"Deng","year":"2020","journal-title":"BMC Endocr. Disord."},{"key":"B33","volume-title":"Bert: Pre-Training of deep bidirectional Transformers for language understanding","author":"Devlin","year":"2018"},{"key":"B34","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1186\/s12859-020-3442-9","article-title":"Machine learning prediction of oncology drug targets based on protein and network properties","volume":"21","author":"Dezs\u0151","year":"2020","journal-title":"BMC Bioinforma."},{"key":"B35","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1158\/1541-7786.MCR-17-0378","article-title":"Precision oncology beyond targeted therapy: Combining omics data with machine learning matches the majority of cancer cells to effective therapeutics","volume":"16","author":"Ding","year":"2018","journal-title":"Mol. Cancer Res."},{"key":"B36","doi-asserted-by":"publisher","first-page":"7112","DOI":"10.1109\/TPAMI.2021.3095381","article-title":"ProtTrans: Toward understanding the language of life through self-supervised learning","volume":"44","author":"Elnaggar","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"B37","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1186\/s12967-017-1285-6","article-title":"In silico prediction of novel therapeutic targets using gene\u2013disease association data","volume":"15","author":"Ferrero","year":"2017","journal-title":"J. Transl. Med."},{"key":"B38","doi-asserted-by":"publisher","first-page":"2823","DOI":"10.1002\/1878-0261.13056","article-title":"Global mapping of cancers: The cancer genome atlas and beyond","volume":"15","author":"Ganini","year":"2021","journal-title":"Mol. Oncol."},{"key":"B39","doi-asserted-by":"publisher","first-page":"1969","DOI":"10.1126\/science.287.5460.1969","article-title":"Mechanism-based target identification and drug discovery in cancer research","volume":"287","author":"Gibbs","year":"2000","journal-title":"Science"},{"key":"B40","doi-asserted-by":"publisher","first-page":"1124","DOI":"10.1021\/ci060003g","article-title":"Prediction of biological targets for compounds using multiple-category Bayesian models trained on chemogenomics databases","volume":"46","author":"Glick","year":"2006","journal-title":"J. Chem. Inf. Model."},{"key":"B41","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1038\/nrd.2016.184","article-title":"Phase II and phase III failures: 2013-2015","volume":"15","author":"Harrison","year":"2016","journal-title":"Nat. Rev. Drug Discov."},{"key":"B42","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.molonc.2012.01.010","article-title":"Cancer biomarkers","volume":"6","author":"Henry","year":"2012","journal-title":"Mol. Oncol."},{"key":"B43","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1186\/s12859-015-0845-0","article-title":"Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory","volume":"17","author":"Huang","year":"2016","journal-title":"BMC Bioinforma."},{"key":"B44","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.gpb.2016.11.005","article-title":"Exploitation of gene expression and cancer biomarkers in paving the path to era of personalized medicine","volume":"15","author":"Kamel","year":"2017","journal-title":"Genomics, proteomics Bioinforma."},{"key":"B45","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1186\/s12859-017-1639-3","article-title":"In silico re-identification of properties of drug target proteins","volume":"18","author":"Kim","year":"2017","journal-title":"BMC Bioinforma."},{"key":"B46","doi-asserted-by":"publisher","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":"B47","doi-asserted-by":"publisher","first-page":"1187","DOI":"10.1093\/bioinformatics\/btaa763","article-title":"DeepGOPlus: Improved protein function prediction from sequence","volume":"37","author":"Kulmanov","year":"2021","journal-title":"Bioinformatics"},{"key":"B48","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1093\/bioinformatics\/btx624","article-title":"DeepGO: Predicting protein functions from sequence and interactions using a deep ontology-aware classifier","volume":"34","author":"Kulmanov","year":"2018","journal-title":"Bioinformatics"},{"key":"B49","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.compbiomed.2014.11.008","article-title":"Identification of human drug targets using machine-learning algorithms","volume":"56","author":"Kumari","year":"2015","journal-title":"Comput. Biol. Med."},{"key":"B50","doi-asserted-by":"publisher","first-page":"707277","DOI":"10.3389\/fonc.2021.707277","article-title":"Tryptophan and its metabolites in lung cancer: Basic functions and clinical significance","author":"Li","year":"2021","journal-title":"Front. Oncol."},{"key":"B51","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.aca.2015.02.032","article-title":"Large-scale identification of potential drug targets based on the topological features of human protein\u2013protein interaction network","volume":"871","author":"Li","year":"2015","journal-title":"Anal. Chim. Acta"},{"key":"B52","doi-asserted-by":"publisher","first-page":"2301","DOI":"10.2174\/1568026619666191016155543","article-title":"Identifying cancer targets based on machine learning methods via Chou\u2019s 5-steps rule and general pseudo components","volume":"19","author":"Liang","year":"2019","journal-title":"Curr. Top. Med. Chem."},{"key":"B53","doi-asserted-by":"publisher","first-page":"i911","DOI":"10.1093\/bioinformatics\/btaa822","article-title":"DeepCDR: A hybrid graph convolutional network for predicting cancer drug response","volume":"36","author":"Liu","year":"2020","journal-title":"Bioinformatics"},{"key":"B54","doi-asserted-by":"publisher","first-page":"4403","DOI":"10.3390\/ijms22094403","article-title":"Kynurenine\/tryptophan Ratio as a potential blood-based biomarker in non-small cell lung cancer","volume":"22","author":"Mandarano","year":"2021","journal-title":"Int. J. Mol. Sci."},{"key":"B55","doi-asserted-by":"publisher","first-page":"28","DOI":"10.3390\/proteomes4030028","article-title":"Omics\u201d-informed drug and biomarker discovery: Opportunities, challenges and future perspectives","volume":"4","author":"Matthews","year":"2016","journal-title":"Proteomes"},{"key":"B56","doi-asserted-by":"publisher","first-page":"2738","DOI":"10.3390\/ijms22052738","article-title":"Identification of overexpressed genes in malignant pleural mesothelioma","volume":"22","author":"Morani","year":"2021","journal-title":"Int. J. Mol. Sci."},{"key":"B57","doi-asserted-by":"publisher","first-page":"e1006701","DOI":"10.1371\/journal.pcbi.1006701","article-title":"New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx","volume":"15","author":"Mounir","year":"2019","journal-title":"PLoS Comput. Biol."},{"key":"B58","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1158\/1541-7786.MCR-19-0239","article-title":"Targeting the kynurenine pathway for the treatment of cisplatin-resistant lung cancer","volume":"18","author":"Nguyen","year":"2020","journal-title":"Mol. Cancer Res."},{"key":"B59","doi-asserted-by":"publisher","first-page":"1030","DOI":"10.3389\/fonc.2020.01030","article-title":"Integrated multi-omics analyses in oncology: A review of machine learning methods and tools","volume":"10","author":"Nicora","year":"2020","journal-title":"Front. Oncol."},{"key":"B60","doi-asserted-by":"publisher","first-page":"D1302","DOI":"10.1093\/nar\/gkaa1027","article-title":"Open targets platform: Supporting systematic drug-target identification and prioritisation","volume":"49","author":"Ochoa","year":"2021","journal-title":"Nucleic Acids Res."},{"key":"B61","doi-asserted-by":"publisher","first-page":"1937","DOI":"10.1093\/bib\/bbz122","article-title":"An omics perspective on drug target discovery platforms","volume":"21","author":"Paananen","year":"2020","journal-title":"Briefings Bioinforma."},{"key":"B62","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/1471-2105-8-5","article-title":"Discovering biomarkers from gene expression data for predicting cancer subgroups using neural networks and relational fuzzy clustering","volume":"8","author":"Pal","year":"2007","journal-title":"BMC Bioinforma."},{"key":"B63","doi-asserted-by":"publisher","first-page":"D77","DOI":"10.1093\/nar\/gkz947","article-title":"Expression atlas update: From tissues to single cells","volume":"48","author":"Papatheodorou","year":"2020","journal-title":"Nucleic Acids Res."},{"key":"B64","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"B65","volume-title":"Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation","author":"Powers","year":"2011"},{"key":"B66","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1186\/s12920-017-0293-y","article-title":"Differential gene expression in disease: A comparison between high-throughput studies and the literature","volume":"10","author":"Rodriguez-Esteban","year":"2017","journal-title":"BMC Med. Genomics"},{"key":"B67","doi-asserted-by":"publisher","first-page":"2345","DOI":"10.1021\/ci700157b","article-title":"y-Randomization and its variants in QSPR\/QSAR","volume":"47","author":"R\u00fccker","year":"2007","journal-title":"J. Chem. Inf. Model."},{"key":"B68","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1186\/s12885-016-2213-5","article-title":"Clinical significance of AGE-RAGE axis in colorectal cancer: Associations with glyoxalase-I, adiponectin receptor expression and prognosis","volume":"16","author":"Sakellariou","year":"2016","journal-title":"BMC Cancer"},{"key":"B69","doi-asserted-by":"publisher","first-page":"107494","DOI":"10.1016\/j.compbiolchem.2021.107494","article-title":"Convolutional neural networks with image representation of amino acid sequences for protein function prediction","volume":"92","author":"Sara","year":"2021","journal-title":"Comput. Biol. Chem."},{"key":"B70","doi-asserted-by":"publisher","first-page":"3163","DOI":"10.1093\/bioinformatics\/bts577","article-title":"InterMine: A flexible data warehouse system for the integration and analysis of heterogeneous biological data","volume":"28","author":"Smith","year":"2012","journal-title":"Bioinformatics"},{"key":"B71","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1038\/s41592-019-0437-4","article-title":"Protein-level assembly increases protein sequence recovery from metagenomic samples manyfold","volume":"16","author":"Steinegger","year":"2019","journal-title":"Nat. Methods"},{"key":"B72","doi-asserted-by":"publisher","first-page":"2542","DOI":"10.1038\/s41467-018-04964-5","article-title":"Clustering huge protein sequence sets in linear time","volume":"9","author":"Steinegger","year":"2018","journal-title":"Nat. Commun."},{"key":"B73","doi-asserted-by":"publisher","first-page":"103799","DOI":"10.1016\/j.jbi.2021.103799","article-title":"Biomedical named entity recognition using BERT in the machine reading comprehension framework","volume":"118","author":"Sun","year":"2021","journal-title":"J. Biomed. Inf."},{"key":"B74","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA a cancer J. Clin."},{"key":"B75","doi-asserted-by":"publisher","first-page":"256","DOI":"10.3389\/fgene.2019.00256","article-title":"A machine learning approach for identifying gene biomarkers guiding the treatment of breast cancer","volume":"10","author":"Tabl","year":"2019","journal-title":"Front. Genet."},{"key":"B76","first-page":"14","article-title":"Computational drug-target interaction prediction based on graph embedding and graph mining","author":"Thafar","year":"2020"},{"key":"B77","doi-asserted-by":"publisher","first-page":"4751","DOI":"10.1038\/s41598-022-08787-9","article-title":"Affinity2Vec: Drug-target binding affinity prediction through representation learning, graph mining, and machine learning","volume":"12","author":"Thafar","year":"2022","journal-title":"Sci. Rep."},{"key":"B78","article-title":"Drug repositioning through the development of diverse computational methods using machine learning, deep learning, and graph mining","author":"Thafar","year":"2022"},{"key":"B79","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1186\/s13321-021-00552-w","article-title":"DTi2Vec: Drug-target interaction prediction using network embedding and ensemble learning","volume":"13","author":"Thafar","year":"2021","journal-title":"J. Cheminform."},{"key":"B80","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1186\/s13321-020-00447-2","article-title":"DTiGEMS+: Drug\u2013target interaction prediction using graph embedding, graph mining, and similarity-based techniques","volume":"12","author":"Thafar","year":"","journal-title":"J. Cheminformatics"},{"key":"B81","doi-asserted-by":"publisher","first-page":"782","DOI":"10.3389\/fchem.2019.00782","article-title":"Comparison study of computational prediction tools for drug-target binding affinities","volume":"7","author":"Thafar","year":"2019","journal-title":"Front. Chem."},{"key":"B82","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1016\/j.omtm.2020.03.023","article-title":"Reversing acute kidney injury using pulsed focused ultrasound and msc therapy: A role for HSP-mediated PI3K\/akt signaling","volume":"17","author":"Ullah","year":"2020","journal-title":"Mol. Ther. Methods Clin. Dev."},{"key":"B83","doi-asserted-by":"publisher","first-page":"D480","DOI":"10.1093\/nar\/gkaa1100","article-title":"UniProt: The universal protein knowledgebase in 2021","volume":"49","author":"Uniprot","year":"2021","journal-title":"Nucleic Acids Res."},{"key":"B84","volume-title":"Attention is all you need. Advances in neural information processing systems","author":"Vaswani","year":"2017"},{"key":"B85","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.jbi.2014.03.009","article-title":"Screening drug target proteins based on sequence information","volume":"49","author":"Wang","year":"2014","journal-title":"J. Biomed. Inf."},{"key":"B86","doi-asserted-by":"publisher","first-page":"D1031","DOI":"10.1093\/nar\/gkz981","article-title":"Therapeutic target database 2020: Enriched resource for facilitating research and early development of targeted therapeutics","volume":"48","author":"Wang","year":"2020","journal-title":"Nucleic Acids Res."},{"key":"B87","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1038\/ng.2764","article-title":"The cancer genome atlas pan-cancer analysis project","volume":"45","author":"Weinstein","year":"2013","journal-title":"Nat. Genet."},{"key":"B88","doi-asserted-by":"publisher","first-page":"D901","DOI":"10.1093\/nar\/gkm958","article-title":"DrugBank: A knowledgebase for drugs, drug actions and drug targets","volume":"36","author":"Wishart","year":"2008","journal-title":"Nucleic Acids Res."},{"key":"B89","doi-asserted-by":"publisher","first-page":"e0125026","DOI":"10.1371\/journal.pone.0125026","article-title":"Circulating MicroRNAs as non-invasive biomarkers for early detection of non-small-cell lung cancer","volume":"10","author":"Wozniak","year":"2015","journal-title":"PLoS One"},{"key":"B90","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1186\/1471-2105-7-262","article-title":"Predicting DNA-binding sites of proteins from amino acid sequence","volume":"7","author":"Yan","year":"2006","journal-title":"BMC Bioinforma."},{"key":"B91","doi-asserted-by":"publisher","first-page":"2595","DOI":"10.12659\/MSM.893472","article-title":"Adiponectin gene polymorphisms are associated with increased risk of colorectal cancer","volume":"21","author":"Yang","year":"2015","journal-title":"Med. Sci. Monit."},{"key":"B92","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1101\/gr.6888208","article-title":"Quantitative systems-level determinants of human genes targeted by successful drugs","volume":"18","author":"Yao","year":"2008","journal-title":"Genome Res."},{"key":"B93","volume-title":"Large batch optimization for deep learning: Training BERT in 76 minutes","author":"You","year":"2019"},{"key":"B94","doi-asserted-by":"publisher","first-page":"103985","DOI":"10.1016\/j.ijmedinf.2019.103985","article-title":"Extracting comprehensive clinical information for breast cancer using deep learning methods","volume":"132","author":"Zhang","year":"2019","journal-title":"Int. J. Med. Inf."}],"container-title":["Frontiers in Genetics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fgene.2023.1139626\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T05:04:10Z","timestamp":1680757450000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fgene.2023.1139626\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,6]]},"references-count":94,"alternative-id":["10.3389\/fgene.2023.1139626"],"URL":"https:\/\/doi.org\/10.3389\/fgene.2023.1139626","relation":{},"ISSN":["1664-8021"],"issn-type":[{"value":"1664-8021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,6]]},"article-number":"1139626"}}