{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T15:42:51Z","timestamp":1774366971832,"version":"3.50.1"},"reference-count":74,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T00:00:00Z","timestamp":1716249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"publisher","award":["61602399"],"award-info":[{"award-number":["61602399"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"publisher","award":["32302138"],"award-info":[{"award-number":["32302138"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"publisher","award":["ZR2020MF100"],"award-info":[{"award-number":["ZR2020MF100"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shandong Provincial Nature Science Foundation, China","award":["61602399"],"award-info":[{"award-number":["61602399"]}]},{"name":"Shandong Provincial Nature Science Foundation, China","award":["32302138"],"award-info":[{"award-number":["32302138"]}]},{"name":"Shandong Provincial Nature Science Foundation, China","award":["ZR2020MF100"],"award-info":[{"award-number":["ZR2020MF100"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Biosensors play a crucial role in detecting cancer signals by orchestrating a series of intricate biological and physical transduction processes. Among various cancers, breast cancer stands out due to its genetic underpinnings, which trigger uncontrolled cell proliferation, predominantly impacting women, and resulting in significant mortality rates. The utilization of biosensors in predicting survival time becomes paramount in formulating an optimal treatment strategy. However, conventional biosensors employing traditional machine learning methods encounter challenges in preprocessing features for the learning task. Despite the potential of deep learning techniques to automatically extract useful features, they often struggle to effectively leverage the intricate relationships between features and instances. To address this challenge, our study proposes a novel smart biosensor architecture that integrates a multi-view multi-way graph learning (MVMWGL) approach for predicting breast cancer survival time. This innovative approach enables the assimilation of insights from gene interactions and biosensor similarities. By leveraging real-world data, we conducted comprehensive evaluations, and our experimental results unequivocally demonstrate the superiority of the MVMWGL approach over existing methods.<\/jats:p>","DOI":"10.3390\/s24113289","type":"journal-article","created":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T07:56:11Z","timestamp":1716364571000},"page":"3289","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Smart Biosensor for Breast Cancer Survival Prediction Based on Multi-View Multi-Way Graph Learning"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6659-5727","authenticated-orcid":false,"given":"Wenming","family":"Ma","sequence":"first","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9580-4878","authenticated-orcid":false,"given":"Mingqi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6975-4271","authenticated-orcid":false,"given":"Zihao","family":"Chu","sequence":"additional","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5632-0101","authenticated-orcid":false,"given":"Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Naresh, V., and Lee, N. (2021). A Review on Biosensors and Recent Development of Nanostructured Materials-Enabled Biosensors. Sensors, 21.","DOI":"10.3390\/s21041109"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Banerjee, A., Maity, S., and Mastrangelo, C.H. (2021). Nanostructures for Biosensing, with a Brief Overview on Cancer Detection, IoT, and the Role of Machine Learning in Smart Biosensors. Sensors, 21.","DOI":"10.3390\/s21041253"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.breast.2022.08.010","article-title":"Current and future burden of breast cancer: Global statistics for 2020 and 2040","volume":"66","author":"Arnold","year":"2022","journal-title":"Breast"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1056\/NEJMra0801289","article-title":"Gene-Expression Signatures in Breast Cancer","volume":"360","author":"Sotiriou","year":"2009","journal-title":"N. Engl. J. Med."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ratosa, I., Plavc, G., Pislar, N., Zagar, T., Perhavec, A., and Franco, P. (2021). Improved Survival after Breast-Conserving Therapy Compared with Mastectomy in Stage I-IIA Breast Cancer. Cancers, 13.","DOI":"10.3390\/cancers13164044"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1007\/s42979-020-00305-w","article-title":"Breast Cancer Prediction: A Comparative Study Using Machine Learning Techniques","volume":"1","author":"Islam","year":"2020","journal-title":"SN Comput. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2000063","DOI":"10.1002\/aisy.202000063","article-title":"Machine Learning-Enabled Smart Sensor Systems","volume":"2","author":"Ha","year":"2020","journal-title":"Adv. Intell. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"L\u00f3pez, N.C., Garc\u00eda-Ord\u00e1s, M.T., Vitelli-Storelli, F., Fern\u00e1ndez-Navarro, P., Palazuelos, C., and Alaiz-Rodr\u00edguez, R. (2021). Evaluation of Feature Selection Techniques for Breast Cancer Risk Prediction. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph182010670"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Murugan, S., Kumar, B.M., and Amudha, S. (2017, January 8\u20139). Classification and Prediction of Breast Cancer using Linear Regression, Decision Tree and Random Forest. Proceedings of the 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), Mysore, India.","DOI":"10.1109\/CTCEEC.2017.8455058"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"551","DOI":"10.4236\/jbise.2013.65070","article-title":"Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic","volume":"6","author":"Nguyen","year":"2013","journal-title":"J. Biomed. Sci. Eng."},{"key":"ref_11","first-page":"100134","article-title":"Deep learning in computer vision: A critical review of emerging techniques and application scenarios","volume":"6","author":"Chai","year":"2021","journal-title":"Mach. Learn. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1109\/TNNLS.2020.2979670","article-title":"A Survey of the Usages of Deep Learning for Natural Language Processing","volume":"32","author":"Otter","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"9411","DOI":"10.1007\/s11042-020-10073-7","article-title":"Automatic Speech Recognition: A Survey","volume":"80","author":"Malik","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.cmpb.2018.10.004","article-title":"A Semi-supervised Deep Learning Method Based on Stacked Sparse Auto-encoder for Cancer Prediction Using RNA-seq Data","volume":"166","author":"Xiao","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.eswa.2018.11.008","article-title":"Convolutional Neural Network Improvement for Breast Cancer Classification","volume":"120","author":"Ting","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lim, H.G., Lee, O.J., Shung, K.K., Kim, J.T., and Kim, H.H. (2020). Classification of Breast Cancer Cells Using the Integration of High-Frequency Single-Beam Acoustic Tweezers and Convolutional Neural Networks. Cancers, 12.","DOI":"10.3390\/cancers12051212"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yao, H., Zhang, X., Zhou, X., and Liu, S. (2019). Parallel Structure Deep Neural Network Using CNN and RNN with an Attention Mechanism for Breast Cancer Histology Image Classification. Cancers, 11.","DOI":"10.3390\/cancers11121901"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Srikantamurthy, M.M., Rallabandi, V.S., Dudekula, D.B., Natarajan, S., and Park, J. (2023). Classification of benign and malignant subtypes of breast cancer histopathology imaging using hybrid CNN-LSTM based transfer learning. BMC Med. Imaging, 23.","DOI":"10.1186\/s12880-023-00964-0"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"13505","DOI":"10.1038\/s41598-021-92799-4","article-title":"Long-term Cancer Survival Prediction Using Multimodal Deep Learning","volume":"11","author":"Rohr","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gudagunti, F.D., Jayasooriya, V., Afrose, S., Nawarathna, D., and Lima, I.T. (2019). Biosensor for the Characterization of Gene Expression in Cells. Chemosensors, 7.","DOI":"10.3390\/chemosensors7040060"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Varnier, R., Sajous, C., de Talhouet, S., Smentek, C., P\u00e9ron, J., You, B., Reverdy, T., and Freyer, G. (2021). Using Breast Cancer Gene Expression Signatures in Clinical Practice: Unsolved Issues, Ongoing Trials and Future Perspectives. Cancers, 13.","DOI":"10.3390\/cancers13194840"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mondol, R.K., Millar, E.K.A., Graham, P.H., Browne, L., Sowmya, A., and Meijering, E. (2023). hist2RNA: An Efficient Deep Learning Architecture to Predict Gene Expression from Breast Cancer Histopathology Images. Cancers, 15.","DOI":"10.3390\/cancers15092569"},{"key":"ref_23","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":"2020","journal-title":"IEEE Trans. NanoBiosci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Qiu, L., Li, H., Wang, M., and Wang, X. (2021). Gated Graph Attention Network for Cancer Prediction. Sensors, 21.","DOI":"10.3390\/s21061938"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Momanyi, B.M., Zhou, Y.W., Grace-Mercure, B.K., Temesgen, S.A., Basharat, A., Ning, L., Tang, L., Gao, H., Lin, H., and Tang, H. (2024). SAGESDA: Multi-GraphSAGE networks for predicting SnoRNA-disease associations. Curr. Res. Struct. Biol., 7.","DOI":"10.1016\/j.crstbi.2023.100122"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, S., Su, X., Zhao, B., Hu, P., Bai, T., and Hu, L. (2023). An Improved Graph Isomorphism Network for Accurate Prediction of Drug\u2014Drug Interactions. Mathematics, 11.","DOI":"10.3390\/math11183990"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1515","DOI":"10.1093\/bib\/bbaa257","article-title":"Biological network analysis with deep learning","volume":"22","author":"Muzio","year":"2020","journal-title":"Briefings Bioinform."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.ymeth.2021.01.004","article-title":"Prediction and Interpretation of Cancer Survival Using Graph Convolution Neural Networks","volume":"192","author":"Ramirez","year":"2021","journal-title":"Methods"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"100267","DOI":"10.1016\/j.talo.2023.100267","article-title":"Recent advancements in machine learning enabled portable and wearable biosensors","volume":"8","author":"Kadian","year":"2023","journal-title":"Talanta Open"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1038\/s42256-021-00360-9","article-title":"Machine learning and computation-enabled intelligent sensor design","volume":"3","author":"Ballard","year":"2021","journal-title":"Nat. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2100734","DOI":"10.1002\/adhm.202100734","article-title":"Machine Learning-Reinforced Noninvasive Biosensors for Healthcare","volume":"10","author":"Zhang","year":"2021","journal-title":"Adv. Healthc. Mater."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Jin, X., Liu, C., Xu, T., Su, L., and Zhang, X. (2020). Artificial intelligence biosensors: Challenges and prospects. Biosens. Bioelectron., 165.","DOI":"10.1016\/j.bios.2020.112412"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3346","DOI":"10.1021\/acssensors.0c01424","article-title":"Advancing biosensors with machine learning","volume":"5","author":"Cui","year":"2020","journal-title":"ACS Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3195","DOI":"10.3233\/JIFS-191622","article-title":"Deep-learning based forecasting sampling frequency of biosensors in wireless body area networks","volume":"39","author":"Mehrani","year":"2020","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Arano-Martinez, J.A., Mart\u00ednez-Gonz\u00e1lez, C.L., Salazar, M.I., and Torres-Torres, C. (2022). A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning. Biosensors, 12.","DOI":"10.3390\/bios12090710"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"100202","DOI":"10.1016\/j.sintl.2022.100202","article-title":"Explainable machine learning of the breast cancer staging for designing smart biomarker sensors","volume":"3","author":"Idrees","year":"2022","journal-title":"Sens. Int."},{"key":"ref_37","first-page":"184","article-title":"Breast Cancer Prediction Model with Decision Tree and Adaptive Boosting","volume":"10","author":"Assegie","year":"2021","journal-title":"IAES Int. J. Artif. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Momenyan, S., Baghestani, A.R., Momenyan, N., Naseri, P., and Akbari, M.E. (2018). Survival prediction of patients with breast cancer: Comparisons of decision tree and logistic regression analysis. Int. J. Cancer Manag., 11.","DOI":"10.5812\/ijcm.9176"},{"key":"ref_39","first-page":"797","article-title":"An improved weighted decision tree approach for breast cancer prediction","volume":"12","author":"Juneja","year":"2020","journal-title":"Int. J. Inf. Technol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"020050","DOI":"10.1063\/1.5132477","article-title":"Random Forest for Breast Cancer Prediction","volume":"2168","author":"Octaviani","year":"2019","journal-title":"AIP Conf. Proc."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"31","DOI":"10.3233\/THC-151071","article-title":"Machine Learning Models in Breast Cancer Survival Prediction","volume":"24","author":"Montazeri","year":"2016","journal-title":"Technol. Health Care"},{"key":"ref_42","first-page":"1","article-title":"SVM and SVM Ensembles in Breast Cancer Prediction","volume":"12","author":"Huang","year":"2017","journal-title":"PLoS ONE"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"230","DOI":"10.4048\/jbc.2012.15.2.230","article-title":"Development of Novel Breast Cancer Recurrence Prediction Model Using Support Vector Machine","volume":"15","author":"Kim","year":"2012","journal-title":"J. Breast Cancer"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2157984","DOI":"10.1155\/2016\/2157984","article-title":"Survival prediction and feature selection in patients with breast cancer using support vector regression","volume":"2016","author":"Goli","year":"2016","journal-title":"Comput. Math. Methods Med."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2925","DOI":"10.1109\/TCE.2023.3301067","article-title":"Low-Rank Tensor Regularized Graph Fuzzy Learning for Multi-View Data Processing","volume":"70","author":"Pan","year":"2024","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Hassanzadeh, H.R., Phan, J.H., and Wang, M.D. (2016). A Multi-Modal Graph-Based Semi-Supervised Pipeline for Predicting Cancer Survival. arXiv.","DOI":"10.1109\/BIBM.2016.7822516"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Mojarad, S.A., Dlay, S.S., Woo, W.L., and Sherbet, G. (2010, January 21\u201323). Breast Cancer Prediction and Cross Validation Using Multilayer Perceptron Neural Networks. Proceedings of the 2010 7th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP 2010), IEEE, Newcastle Upon Tyne, UK.","DOI":"10.1109\/CSNDSP16145.2010.5580318"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1093\/comjnl\/bxz051","article-title":"A Novel Data Mining on Breast Cancer Survivability Using MLP Ensemble Learners","volume":"63","author":"Salehi","year":"2019","journal-title":"Comput. J."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1093\/comjnl\/bxaa109","article-title":"Breast Cancer Diagnosis Using Multi-Stage Weight Adjustment in the MLP Neural Network","volume":"65","author":"Rezaeipanah","year":"2020","journal-title":"Comput. J."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Chen, X., Zhang, K., Abdoli, N., Gilley, P.W., Wang, X., Liu, H., Zheng, B., and Qiu, Y. (2022). Transformers Improve Breast Cancer Diagnosis from Unregistered Multi-View Mammograms. Diagnostics, 12.","DOI":"10.20944\/preprints202206.0315.v1"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Tong, L., Mitchel, J., Chatlin, K., and Wang, M.D. (2020). Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis. BMC Med. Inform. Decis. Mak., 20.","DOI":"10.1186\/s12911-020-01225-8"},{"key":"ref_52","first-page":"316","article-title":"Multi-class breast cancer classification using deep learning convolutional neural network","volume":"9","author":"Nawaz","year":"2018","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"11383","DOI":"10.1007\/s00521-020-05394-5","article-title":"Convolutional Neural Network-based Models for Diagnosis of Breast Cancer","volume":"34","author":"Masud","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"163","DOI":"10.2478\/acss-2020-0018","article-title":"Breast Cancer Prediction Using Stacked GRU-LSTM-BRNN","volume":"25","author":"Dutta","year":"2020","journal-title":"Appl. Comput. Syst."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2601","DOI":"10.1093\/bioinformatics\/btab140","article-title":"Integrative survival analysis of breast cancer with gene expression and DNA methylation data","volume":"37","author":"Bichindaritz","year":"2021","journal-title":"Bioinformatics"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"102439","DOI":"10.1016\/j.ipm.2020.102439","article-title":"Improved Breast Cancer Classification Through Combining Graph Convolutional Network and Convolutional Neural Network","volume":"58","author":"Zhang","year":"2021","journal-title":"Inf. Process. Manag."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1186\/s13073-021-00845-7","article-title":"Explaining decisions of graph convolutional neural networks: Patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer","volume":"13","author":"Chereda","year":"2021","journal-title":"Genome Med."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Gao, J., Lyu, T., Xiong, F., Wang, J., Ke, W., and Li, Z. (2020, January 25\u201330). MGNN: A Multimodal Graph Neural Network for Predicting the Survival of Cancer Patients. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, Virtual.","DOI":"10.1145\/3397271.3401214"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1109\/TCBB.2021.3083566","article-title":"Predicting the Survival of Cancer Patients With Multimodal Graph Neural Network","volume":"19","author":"Gao","year":"2022","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Li, B., and Nabavi, S. (2024). A multimodal graph neural network framework for cancer molecular subtype classification. BMC Bioinform., 25.","DOI":"10.1186\/s12859-023-05622-4"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1363896","DOI":"10.3389\/fgene.2024.1363896","article-title":"Classifying breast cancer using multi-view graph neural network based on multi-omics data","volume":"15","author":"Ren","year":"2024","journal-title":"Front. Genet."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"3117","DOI":"10.1109\/TCBB.2023.3290394","article-title":"Patient Graph Deep Learning to Predict Breast Cancer Molecular Subtype","volume":"20","author":"Furtney","year":"2023","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Hao, Y., Jing, X.Y., and Sun, Q. (2022). Joint learning sample similarity and correlation representation for cancer survival prediction. BMC Bioinform., 23.","DOI":"10.1186\/s12859-022-05110-1"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Li, Z., Jiang, Y., Liu, L., Xia, Y., and Li, R. (2023, January 8). Single-Cell Spatial Analysis of Histopathology Images for Survival Prediction via Graph Attention Network. Proceedings of the International Workshop on Applications of Medical AI, Vancouver, BC, Canada.","DOI":"10.1007\/978-3-031-47076-9_12"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"pl1","DOI":"10.1126\/scisignal.2004088","article-title":"Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal","volume":"6","author":"Gao","year":"2013","journal-title":"Sci. Signal."},{"key":"ref_66","first-page":"D489","article-title":"Pathway Commons 2019 Update: Integration, analysis and exploration of pathway data","volume":"48","author":"Rodchenkov","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Alfonso Perez, G., and Castillo, R. (2023). Nonlinear Techniques and Ridge Regression as a Combined Approach: Carcinoma Identification Case Study. Mathematics, 11.","DOI":"10.3390\/math11081795"},{"key":"ref_68","first-page":"2632","article-title":"Expert cancer model using supervised algorithms with a LASSO selection approach","volume":"11","author":"Ghosh","year":"2021","journal-title":"Int. J. Electr. Comput. Eng."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Das, J., Gayvert, K.M., Bunea, F., Wegkamp, M.H., and Yu, H. (2015). ENCAPP: Elastic-net-based prognosis prediction and biomarker discovery for human cancers. BMC Genom., 16.","DOI":"10.1186\/s12864-015-1465-9"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Zare, H., Haffari, G., Gupta, A., and Brinkman, R.R. (2013). Scoring relevancy of features based on combinatorial analysis of Lasso with application to lymphoma diagnosis. BMC Genom., 14.","DOI":"10.1186\/1471-2164-14-S1-S14"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"3788","DOI":"10.1093\/bioinformatics\/btaa239","article-title":"Blood-based multi-tissue gene expression inference with Bayesian ridge regression","volume":"36","author":"Xu","year":"2020","journal-title":"Bioinformatics"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"100312","DOI":"10.1016\/j.jcpo.2021.100312","article-title":"Low-value care and excess out-of-pocket expenditure among older adults with incident cancer\u2014A machine learning approach","volume":"30","author":"Iloabuchi","year":"2021","journal-title":"J. Cancer Policy"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.swevo.2016.02.002","article-title":"Microarray medical data classification using kernel ridge regression and modified cat swarm optimization based gene selection system","volume":"28","author":"Mohapatra","year":"2016","journal-title":"Swarm Evol. Comput."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"115412","DOI":"10.1016\/j.cma.2022.115412","article-title":"Bayesian inference using Gaussian process surrogates in cancer modeling","volume":"399","author":"Rocha","year":"2022","journal-title":"Comput. Methods Appl. Mech. Eng."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3289\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:46:04Z","timestamp":1760107564000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/11\/3289"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,21]]},"references-count":74,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["s24113289"],"URL":"https:\/\/doi.org\/10.3390\/s24113289","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,21]]}}}