{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T07:34:17Z","timestamp":1767771257385,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,10]],"date-time":"2021-03-10T00:00:00Z","timestamp":1615334400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005270","name":"Fujian Provincial Department of Science and Technology","doi-asserted-by":"publisher","award":["2019H0001"],"award-info":[{"award-number":["2019H0001"]}],"id":[{"id":"10.13039\/501100005270","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61702432"],"award-info":[{"award-number":["61702432"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental 385 Research Funds for Central Universities of China","award":["20720180070"],"award-info":[{"award-number":["20720180070"]}]},{"name":"International Cooperation 386 Projects of Fujian Province in China","award":["2018I0016"],"award-info":[{"award-number":["2018I0016"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With its increasing incidence, cancer has become one of the main causes of worldwide mortality. In this work, we mainly propose a novel attention-based neural network model named Gated Graph ATtention network (GGAT) for cancer prediction, where a gating mechanism (GM) is introduced to work with the attention mechanism (AM), to break through the previous work\u2019s limitation of 1-hop neighbourhood reasoning. In this way, our GGAT is capable of fully mining the potential correlation between related samples, helping for improving the cancer prediction accuracy. Additionally, to simplify the datasets, we propose a hybrid feature selection algorithm to strictly select gene features, which significantly reduces training time without affecting prediction accuracy. To the best of our knowledge, our proposed GGAT achieves the state-of-the-art results in cancer prediction task on LIHC, LUAD, KIRC compared to other traditional machine learning methods and neural network models, and improves the accuracy by 1% to 2% on Cora dataset, compared to the state-of-the-art graph neural network methods.<\/jats:p>","DOI":"10.3390\/s21061938","type":"journal-article","created":{"date-parts":[[2021,3,10]],"date-time":"2021-03-10T20:51:42Z","timestamp":1615409502000},"page":"1938","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Gated Graph Attention Network for Cancer Prediction"],"prefix":"10.3390","volume":"21","author":[{"given":"Linling","family":"Qiu","sequence":"first","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen 361001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3344-8691","authenticated-orcid":false,"given":"Han","family":"Li","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen 361001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8639-7675","authenticated-orcid":false,"given":"Meihong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen 361001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8677-9080","authenticated-orcid":false,"given":"Xiaoli","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Informatics, Xiamen University, Xiamen 361001, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"394","DOI":"10.3322\/caac.21492","article-title":"Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"68","author":"Bray","year":"2018","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","first-page":"87","article-title":"Interpretation on the report of Global Cancer Statistics 2018","volume":"5","author":"Ning","year":"2019","journal-title":"Electron. J. Compr. Cancer Ther."},{"key":"ref_3","first-page":"1463","article-title":"Ten Methods of Traditional Chinese Medical Cancer Prevention","volume":"12","author":"Rongtao","year":"2008","journal-title":"Mod. Distance Educ. Chin. Tradit. Chin. Med."},{"key":"ref_4","first-page":"1121","article-title":"Review of the application and advantages and disadvantages of sequencing technology in gene diagnosis","volume":"36","author":"Yifu","year":"2014","journal-title":"Hereditas:bjing"},{"key":"ref_5","first-page":"436","article-title":"Naive Bayesian-based nomogram for prediction of prostate cancer recurrence","volume":"68","author":"Demsar","year":"1999","journal-title":"Stud. Health Technol. Inform."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hong, J.H., and Cho, S.B. (2006, January 3\u20136). Multi-class cancer classification with OVR-support vector machines selected by naive Bayes classifier. Proceedings of the International Conference on Neural Information Processing, Hong Kong, China.","DOI":"10.1007\/11893295_18"},{"key":"ref_7","unstructured":"Sarkar, M., and Leong, T.Y. (2000, January 4\u20138). Application of K-nearest neighbors algorithm on breast cancer diagnosis problem. Proceedings of the AMIA Symposium. American Medical Informatics Association, Los Angeles, CA, USA."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yoo, S.H., and Cho, S.B. (2004, January 9\u201313). Optimal gene selection for cancer classification with partial correlation and k-nearest neighbor classifier. Proceedings of the Pacific Rim International Conference on Artificial Intelligence, Auckland, New Zealand.","DOI":"10.1007\/978-3-540-28633-2_75"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"876545","DOI":"10.1155\/2012\/876545","article-title":"Using the K-nearest neighbor algorithm for the classification of lymph node metastasis in gastric cancer","volume":"2012","author":"Li","year":"2012","journal-title":"Comput. Math. Methods Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/S0933-3657(02)00086-6","article-title":"A combined neural network and decision trees model for prognosis of breast cancer relapse","volume":"27","year":"2003","journal-title":"Artif. Intell. Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/s10916-007-9064-1","article-title":"Predicting metastasis in breast cancer: Comparing a decision tree with domain experts","volume":"31","author":"Razavi","year":"2007","journal-title":"J. Med. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1111\/j.1468-0394.2010.00522.x","article-title":"Cascade of genetic algorithm and decision tree for cancer classification on gene expression data","volume":"27","author":"Yeh","year":"2010","journal-title":"Expert Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1090\/dimacs\/055\/01","article-title":"Breast cancer survival and chemotherapy: A support vector machine analysis","volume":"55","author":"Lee","year":"2000","journal-title":"DIMACS Ser. Discret. Math. Theor. Comput. Sci."},{"key":"ref_14","unstructured":"Liu, W., Shen, P., Qu, Y., and Xia, D. (2001, January 10\u201312). Fast algorithm of support vector machines in lung cancer diagnosis. Proceedings of the International Workshop on Medical Imaging and Augmented Reality, Hong Kong, China."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1021\/ci0256438","article-title":"Diagnosing breast cancer based on support vector machines","volume":"43","author":"Liu","year":"2003","journal-title":"J. Chem. Inf. Comput. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.neucom.2003.09.001","article-title":"Cancer recognition with bagged ensembles of support vector machines","volume":"56","author":"Valentini","year":"2004","journal-title":"Neurocomputing"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Nguyen, H.N., Vu, T.N., Ohn, S.Y., Park, Y.M., Han, M.Y., and Kim, C.W. (2006, January 13\u201317). Feature elimination approach based on random forest for cancer diagnosis. Proceedings of the Mexican International Conference on Artificial Intelligence, Apizaco, Mexico.","DOI":"10.1007\/11925231_50"},{"key":"ref_18","unstructured":"Okun, O., and Priisalu, H. (2007, January 6\u20138). Random forest for gene expression based cancer classification: Overlooked issues. Proceedings of the Iberian Conference on Pattern Recognition and Image Analysis, Girona, Spain."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Maliha, S.K., Ema, R.R., Ghosh, S.K., Ahmed, H., Mollick, M.R.J., and Islam, T. (2019, January 6\u20138). Cancer Disease Prediction Using Naive Bayes, K-Nearest Neighbor and J48 algorithm. Proceedings of the 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kanpur, India.","DOI":"10.1109\/ICCCNT45670.2019.8944686"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"To\u011fa\u00e7ar, M., and Ergen, B. (2018, January 28\u201330). Deep learning approach for classification of breast cancer. Proceedings of the 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Turkey.","DOI":"10.1109\/IDAP.2018.8620802"},{"key":"ref_21","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":"ref_22","doi-asserted-by":"crossref","unstructured":"Selvathi, D., and Poornila, A.A. (2018). Deep learning techniques for breast cancer detection using medical image analysis. Biologically Rationalized Computing Techniques for Image Processing Applications, Springer.","DOI":"10.1007\/978-3-319-61316-1_8"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Munir, K., Elahi, H., Ayub, A., Frezza, F., and Rizzi, A. (2019). Cancer diagnosis using deep learning: A bibliographic review. Cancers, 11.","DOI":"10.3390\/cancers11091235"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wu, B., Kausar, T., Xiao, Q., Wang, M., Wang, W., Fan, B., and Sun, D. (2017, January 11\u201313). FF-CNN: An efficient deep neural network for mitosis detection in breast cancer histological images. Proceedings of the Annual Conference on Medical Image Understanding and Analysis, Edinburgh, UK.","DOI":"10.1007\/978-3-319-60964-5_22"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.compmedimag.2018.09.004","article-title":"SD-CNN: A shallow-deep CNN for improved breast cancer diagnosis","volume":"70","author":"Gao","year":"2018","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, Y., Sun, L., Ma, K., and Fang, J. (2018, January 27\u201329). Breast cancer microscope image classification based on CNN with image deformation. Proceedings of the International Conference Image Analysis and Recognition, P\u00f3voa de Varzim, Portugal.","DOI":"10.1007\/978-3-319-93000-8_96"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"105146","DOI":"10.1109\/ACCESS.2019.2892795","article-title":"Breast cancer detection using extreme learning machine based on feature fusion with CNN deep features","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"128613","DOI":"10.1109\/ACCESS.2020.3008868","article-title":"PROMETEO: A CNN-Based Computer-Aided Diagnosis System for WSI Prostate Cancer Detection","volume":"8","year":"2020","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-8-91","article-title":"Modeling human cancer-related regulatory modules by GA-RNN hybrid algorithms","volume":"8","author":"Chiang","year":"2007","journal-title":"BMC Bioinform."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13755-019-0077-1","article-title":"Automated AJCC staging of non-small cell lung cancer (NSCLC) using deep convolutional neural network (CNN) and recurrent neural network (RNN)","volume":"7","author":"Moitra","year":"2019","journal-title":"Health Inf. Sci. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lane, N., and Kahanda, I. (2020, January 16\u201319). DeepACPpred: A Novel Hybrid CNN-RNN Architecture for Predicting Anti-Cancer Peptides. Proceedings of the International Conference on Practical Applications of Computational Biology & Bioinformatics, L\u2019Aquila, Italy.","DOI":"10.1007\/978-3-030-54568-0_7"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/MSP.2017.2693418","article-title":"Geometric deep learning: Going beyond euclidean data","volume":"34","author":"Bronstein","year":"2017","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_33","unstructured":"Kipf, T.N., and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv."},{"key":"ref_34","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y. (2017). Graph attention networks. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"104195","DOI":"10.1016\/j.ijmedinf.2020.104195","article-title":"Using machine learning to predict ovarian cancer","volume":"141","author":"Lu","year":"2020","journal-title":"Int. J. Med. Inform."},{"key":"ref_37","first-page":"e2","article-title":"Data Driven Prognosis of Cervical Cancer Using Class Balancing and Machine Learning Techniques","volume":"7","author":"Arora","year":"2020","journal-title":"EAI Endorsed Trans. Energy Web"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"204309","DOI":"10.1109\/ACCESS.2020.3036912","article-title":"Breast Cancer\u2013Detection System Using PCA, Multilayer Perceptron, Transfer Learning, and Support Vector Machine","volume":"8","author":"Chiu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Montelongo Gonz\u00e1lez, E.E., Reyes Ortiz, J.A., and Gonz\u00e1lez Beltr\u00e1n, B.A. (2020). Machine Learning Models for Cancer Type Classification with Unstructured Data. Computaci\u00f3n y Sistemas, 24.","DOI":"10.13053\/cys-24-2-3367"},{"key":"ref_40","unstructured":"Shiqi, L., Jun, Z., and Shuxun, W. (2019, January 15\u201317). Research on Colorectal Cancer Prediction and Survival Analysis with Data Fusion Based on Deep Learning. Proceedings of the 9th International Workshop on Computer Science and Engineering (WCSE 2019), Hong Kong, China."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Graham, S., Alemi Koohbanani, N., Shaban, M., Heng, P.A., and Rajpoot, N. (2019, January 27\u201328). Cgc-net: Cell graph convolutional network for grading of colorectal cancer histology images. Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, Seoul, Korea.","DOI":"10.1109\/ICCVW.2019.00050"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Schulte-Sasse, R., Budach, S., Hnisz, D., and Marsico, A. (2019, January 17\u201319). Graph Convolutional networks improve the prediction of cancer driver genes. Proceedings of the International Conference on Artificial Neural Networks, Munich, Germany.","DOI":"10.1007\/978-3-030-30493-5_60"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1109\/TNB.2019.2936398","article-title":"A Cancer Survival Prediction Method Based on Graph Convolutional Network","volume":"19","author":"Wang","year":"2019","journal-title":"IEEE Trans. Nanobiosci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"203","DOI":"10.3389\/fphy.2020.00203","article-title":"Classification of Cancer Types Using Graph Convolutional Neural Networks","volume":"8","author":"Ramirez","year":"2020","journal-title":"Front. Phys."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","article-title":"Gene selection for cancer classification using support vector machines","volume":"46","author":"Guyon","year":"2002","journal-title":"Mach. Learn."},{"key":"ref_46","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/6\/1938\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:35:56Z","timestamp":1760160956000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/6\/1938"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,10]]},"references-count":46,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["s21061938"],"URL":"https:\/\/doi.org\/10.3390\/s21061938","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,3,10]]}}}