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Machine learning applications in cancer prognosis and prediction
Open accessAbstract
Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.
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“traditional ML models, such as support vector machines (SVM), decision trees, and random forests, have supported tumor classification, survival prediction, and risk stratification using structured clinical or genomic data [6,12,13,14].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [13]
In the citing paper
“A second well-known challenge is that modern machine learning models, including deep neural networks, are black boxes, devices which focus on predicting outputs from inputs without regard for the mechanism or rationale by which a particular outcome is brought about.”
From Visible Machine Learning for Biomedicine (2018) · cites this paper as Kourou et al., 2014
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“Similarly, in biomedicine, many machine-learning methods are being developed to predict patient outcomes (Kourou et al., 2014), but these approaches typically do not link predictions to underlying mechanisms (Figure 1B).”
From Visible Machine Learning for Biomedicine (2018) · cites this paper as Kourou et al., 2014
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“Machine-learning systems face two recognized challenges that become particularly acute in biomedical applications. The first is input heterogeneity.”
From Visible Machine Learning for Biomedicine (2018) · cites this paper as Kourou et al., 2014
In the citing paper
“For example, cancer can arise as the result of many different combinations of genetic alterations involving many potential genes, any one of which may be mutated only rarely; as a consequence, each new patient presents a distinct constellation of molecular changes never before seen in nature (Alvarez et al., 2016; Kourou et al., 2014).”
From Visible Machine Learning for Biomedicine (2018) · cites this paper as Kourou et al., 2014
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Cites
Hallmarks of Cancer: The Next Generation
Hanahan, D., & Weinberg, R. A. (2011) · Cell · doi:10.1016/j.cell.2011.02.013 · Cited by 66,590
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,537
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066
Wrappers for feature subset selection
Kohavi, R., & John, G. H. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00043-x · Cited by 9,096
Chiu, S., & Tavella, D. (2008) · Routledge eBooks · doi:10.4324/9780080878096-12 · Cited by 6,987
A Multigene Assay to Predict Recurrence of Tamoxifen-Treated, Node-Negative Breast Cancer
Paik, S., Shak, S., Tang, G., Kim, C., Baker, J. B., Cronin, M. T., Baehner, F. L., Walker, M. G., Watson, D., Park, T., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa041588 · Cited by 6,321
Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes
Parker, J. S., Mullins, M., Cheang, M. C. U., Leung, S. C. Y., Voduc, D. K., Vickery, T. L., Davies, S. R., Fauron, C. M. ‐., He, X., Hu, Z., et al. (2009) · Journal of Clinical Oncology · doi:10.1200/jco.2008.18.1370 · Cited by 4,818
Large Margin DAG's for Multiclass Classification
Platt, J. C., Cristianini, N., & Shawe-Taylor, J. S. (1999) · Cited by 1,637
NCBI GEO: mining tens of millions of expression profiles--database and tools update
Barrett, T., Troup, D. B., Wilhite, S. E., Ledoux, P., Rudnev, D., Evangelista, C., Kim, I. F., Soboleva, A., Tomashevsky, M., & Edgar, R. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl887 · Cited by 1,524
Applications of Machine Learning in Cancer Prediction and Prognosis
Cruz, J. A., & Wishart, D. S. (2006) · Cancer Informatics · doi:10.1177/117693510600200030 · Cited by 1,371
Flagged first, then by citations
Research works printed but not linked: 5
- Bishop C.M.: Pattern recognition and machine learning. New York: Springer 2006.
- Mitchell T.M.: The discipline of machine learning: Carnegie Mellon University. : Carnegie Mellon University, School of Computer Science, Machine Learning Department 2006.
- Platt J.C., Cristianini N. and Shawe-Taylor J.: Large margin DAGs for multiclass classification. : 1999: 547.
- Adams S.: Is Coursera the beginning of the end for traditional higher education?. : Higher Education 2012.
- Howlader N., Noone A., Krapcho M., Garshell J., Neyman N. and Aletkruse S.: SEER Cancer Statistics Review, 1975–2010, [Online] National Cancer Institute. Bethesda, MD: National Cancer Institute 2013. [Online].
Cited by
Sorted by citations
Items 101–200 of 1,006
Mahmood, H., Shaban, M., Indave, B. I., Santos‐Silva, A. R., Rajpoot, N., & Khurram, S. A. (2020) · Oral Oncology · doi:10.1016/j.oraloncology.2020.104885 · Cited by 169
Visible Machine Learning for Biomedicine
Yu, M. K., Ma, J., Fisher, J., Kreisberg, J. F., Raphael, B. J., & Ideker, T. (2018) · Cell · doi:10.1016/j.cell.2018.05.056 · Cited by 162
Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy
Raman, R., Srinivasan, S., Virmani, S., Sivaprasad, S., Rao, C., & Rajalakshmi, R. (2018) · Eye · doi:10.1038/s41433-018-0269-y · Cited by 163
Comparing supervised and semi-supervised Machine Learning Models on Diagnosing Breast Cancer
Al‐Azzam, N. Z., & Shatnawi, I. (2021) · Annals of Medicine and Surgery · doi:10.1016/j.amsu.2020.12.043 · Cited by 159
Painuli, D., Bhardwaj, S., & Köse, U. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105580 · Cited by 163
Krithiga, R., & Geetha, P. (2020) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-020-09470-w · Cited by 165
Machine learning in the prediction of cancer therapy
Rafique, R., Islam, S. M. R., & Kazi, J. U. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.07.003 · Cited by 156
Decision support systems for personalized and participative radiation oncology
Lambin, P., Zindler, J. D., Vanneste, B. G. L., Voorde, L. V. D., Eekers, D. B. P., Compter, I., Panth, K. M., Peerlings, J., Larue, R. T. H. M., Deist, T. M., et al. (2016) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2016.01.006 · Cited by 162
Eaton, J. E., Vesterhus, M. N., McCauley, B. M., Atkinson, E. J., Schlicht, E. M., Juran, B. D., Gossard, A. A., LaRusso, N. F., Gores, G. J., Karlsen, T. H., et al. (2018) · Hepatology · doi:10.1002/hep.30085 · Cited by 158
Machine learning: a powerful tool for gene function prediction in plants
Mahood, E. H., Kruse, L. H., & Moghe, G. D. (2020) · Applications in Plant Sciences · doi:10.1002/aps3.11376 · Cited by 153
Machine Learning in Amyotrophic Lateral Sclerosis: Achievements, Pitfalls, and Future Directions
Grollemund, V., Pradat, P., Querin, G., Delbot, F., Chat, G. L., Pradat‐Peyre, J., & Bede, P. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00135 · Cited by 165
Machine learning to detect signatures of disease in liquid biopsies – a user's guide
Ko, J., Baldassano, S. N., Loh, P., Kording, K. P., Litt, B., & Issadore, D. (2017) · Lab on a Chip · doi:10.1039/c7lc00955k · Cited by 154
Wang, D., Zhang, Y., & Zhao, Y. (2017) · International Conference on Computational Biology and Bioinformatics (ICCBB) · doi:10.1145/3155077.3155079 · Cited by 150
A Review of Machine Learning Algorithms for Biomedical Applications
Binson, V. A., Thomas, S., Subramoniam, M., Arun, J., Naveen, S., & Madhu, S. (2024) · Annals of Biomedical Engineering · doi:10.1007/s10439-024-03459-3 · Cited by 155
Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective
Sufyan, M., Shokat, Z., & Ashfaq, U. A. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107356 · Cited by 170
Intelligent Hybrid Deep Learning Model for Breast Cancer Detection
Wang, X., Ahmad, I., Javeed, D., Zaidi, S. F. B., Alotaibi, F. M., Ghoneim, M. E., Daradkeh, Y. I., Asghar, J., & Eldin, S. M. (2022) · Electronics · doi:10.3390/electronics11172767 · Cited by 147
Variational autoencoders for cancer data integration: design principles and computational practice
Simidjievski, N., Bodnar, C., Tariq, I., Scherer, P., Andres Terre, H., Shams, Z., Jamnik, M., & Liò, P. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.01205 · Cited by 145
Dzobo, K., Adotey, S. K., Thomford, N. E., & Dzobo, W. (2019) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2019.0038 · Cited by 157
Virtual reality: A new method to investigate cognitive load during navigation
Armougum, A., Orriols, É., Gaston‐Bellegarde, A., Marle, C. J.-L., & Piolino, P. (2019) · Journal of Environmental Psychology · doi:10.1016/j.jenvp.2019.101338 · Cited by 160
Preventing undesirable behavior of intelligent machines
Thomas, P. S., da Silva, B. C., Barto, A. G., Giguere, S., Brun, Y., & Brunskill, E. (2019) · Science · doi:10.1126/science.aag3311 · Cited by 156
Improving random forest predictions in small datasets from two-phase sampling designs
Han, S., Williamson, B. D., & Fong, Y. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01688-3 · Cited by 155
Lynam, A. L., Dennis, J. M., Owen, K. R., Oram, R. A., Jones, A. G., Shields, B. M., & Ferrat, L. A. (2020) · Diagnostic and Prognostic Research · doi:10.1186/s41512-020-00075-2 · Cited by 142
Breast Cancer Prediction using varying Parameters of Machine Learning Models
Gupta, P., & Garg, S. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.04.064 · Cited by 142
Homology modeling in the time of collective and artificial intelligence
Hameduh, T., Haddad, Y., Adam, V., & Heger, Z. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.11.007 · Cited by 146
Transfer Learning for Molecular Cancer Classification Using Deep Neural Networks
Sevakula, R. K., Singh, V., Verma, N. K., Kumar, C., & Cui, Y. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2822803 · Cited by 147
Study of AdaBoost and Gradient Boosting Algorithms for Predictive Analytics
Bahad, P., & Saxena, P. (2019) · Algorithms for intelligent systems · doi:10.1007/978-981-15-0633-8_22 · Cited by 141
Naive Bayes classifier – An ensemble procedure for recall and precision enrichment
Peretz, O., Koren, M., & Koren, O. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108972 · Cited by 141
Mohebbian, M. R., Marateb, H. R., Mansourian, M., Mañanas, M. Á., & Mokarian, F. (2016) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2016.11.004 · Cited by 142
Nemati, M., Ansary, J., & Nemati, N. (2020) · Patterns · doi:10.1016/j.patter.2020.100074 · Cited by 139
Widman, A. J., Shah, M. J., Frydendahl, A., Halmos, D., Khamnei, C. C., Øgaard, N., Rajagopalan, S., Arora, A., Deshpande, A. S., Hooper, W. F., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03040-4 · Cited by 148
Application of Artificial Intelligence in Lung Cancer
Chiu, H.-Y., Chao, H.-S., & Chen, Y.-M. (2022) · Cancers · doi:10.3390/cancers14061370 · Cited by 138
Self-Learning Random Forests Model for Mapping Groundwater Yield in Data-Scarce Areas
Sameen, M. I., Pradhan, B., & Lee, S. (2018) · Natural Resources Research · doi:10.1007/s11053-018-9416-1 · Cited by 139
Artificial intelligence methods for the diagnosis of breast cancer by image processing: a review
Sadoughi, F., Kazemy, Z., Hamedan, F., Owji, L., Katigari, M. R., & Azadboni, T. T. (2018) · Breast Cancer Targets and Therapy · doi:10.2147/bctt.s175311 · Cited by 138
Classification of Cancer Types Using Graph Convolutional Neural Networks
Ramirez, R., Chiu, Y., Hererra, A., Mostavi, M., Ramirez, J., Chen, Y., Huang, Y., & Jin, Y.-F. (2020) · Frontiers in Physics · doi:10.3389/fphy.2020.00203 · Cited by 136
Predicting Breast Cancer Recurrence Using Machine Learning Techniques
Abreu, P. H., Santos, M. S., Abreu, M. H., Andrade, B., & Silva, D. C. (2016) · ACM Computing Surveys · doi:10.1145/2988544 · Cited by 142
Huang, Y., Talwar, A., Chatterjee, S., & Aparasu, R. R. (2021) · BMC Medical Research Methodology · doi:10.1186/s12874-021-01284-z · Cited by 149
Machine learning applications for prediction of relapse in childhood acute lymphoblastic leukemia
Pan, L., Liu, G., Lin, F., Zhong, S., Xia, H., Sun, X., & Liang, H. (2017) · Scientific Reports · doi:10.1038/s41598-017-07408-0 · Cited by 142
Applications and Techniques of Machine Learning in Cancer Classification: A Systematic Review
Yaqoob, A., Aziz, R. M., & Verma, N. K. (2023) · Human-Centric Intelligent Systems · doi:10.1007/s44230-023-00041-3 · Cited by 134
Kim, H., Goo, J. M., Lee, K. H., Kim, Y. T., & Park, C. M. (2020) · Radiology · doi:10.1148/radiol.2020192764 · Cited by 131
Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia
Winchester, L. M., Harshfield, E. L., Shi, L., Badhwar, A., Khleifat, A. A., Clarke, N., Dehsarvi, A., Lengyel, I., Lourida, I., Madan, C. R., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13390 · Cited by 130
Nithya, B., & Ilango, V. (2019) · SN Applied Sciences · doi:10.1007/s42452-019-0645-7 · Cited by 130
Applying machine learning approach in recycling
Özdemir, M. E., Ali, Z., Subeshan, B., & Asmatulu, E. (2021) · Journal of Material Cycles and Waste Management · doi:10.1007/s10163-021-01182-y · Cited by 138
Ming, C., Viassolo, V., Probst-Hensch, N. M., Chappuis, P. O., Dinov, I. D., & Katapodi, M. C. (2019) · Breast Cancer Research · doi:10.1186/s13058-019-1158-4 · Cited by 131
Radiomics and Machine Learning for Radiotherapy in Head and Neck Cancers
Giraud, P., Giraud, P., Gasnier, A., Ayachy, R. E., Kreps, S., Foy, J., Durdux, C., Huguet, F., Burgun, A., & Bibault, J. (2019) · Frontiers in Oncology · doi:10.3389/fonc.2019.00174 · Cited by 127
Xu, L., Tetteh, G., Lipková, J., Zhao, Y. S., Li, H., Christ, P. F., Piraud, M., Buck, A. K., Shi, K., & Menze, B. H. (2018) · Contrast Media & Molecular Imaging · doi:10.1155/2018/2391925 · Cited by 127
Skin Cancer Disease Detection Using Transfer Learning Technique
Rashid, J., Ishfaq, M., Ali, G., Saeed, M. R., Hussain, M., Alkhalifah, T., Alturise, F., & Samand, N. (2022) · Applied Sciences · doi:10.3390/app12115714 · Cited by 138
Large-Scale Automatic Feature Selection for Biomarker Discovery in High-Dimensional OMICs Data
Leclercq, M., Vittrant, B., Martin‐Magniette, M., Scott‐Boyer, M., Périn, O., Bergeron, A., Fradet, Y., & Droit, A. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00452 · Cited by 125
Feature selection and classification in breast cancer prediction using IoT and machine learning
Gopal, V. N., Al-Turjman, F. M., Kumar, R., Anand, L. G., & Rajesh, M. (2021) · Measurement · doi:10.1016/j.measurement.2021.109442 · Cited by 125
Faisal, M. I., Bashir, S., Khan, Z., & Khan, F. H. (2018) · International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST) · doi:10.1109/iceest.2018.8643311 · Cited by 124
Wang, M. W. H., Goodman, J. M., & Allen, T. E. H. (2020) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.0c00316 · Cited by 133
Application of artificial intelligence and machine learning for prediction of oral cancer risk
Alhazmi, A. S., Alhazmi, Y. A., Makrami, A. M., Masmali, A. M., Salawi, N., Masmali, K., & Patil, S. (2021) · Journal of Oral Pathology and Medicine · doi:10.1111/jop.13157 · Cited by 121
The contribution of artificial intelligence to reducing the diagnostic delay in oral cancer
İlhan, B., Güneri, P., & Wilder-Smith, P. B. B. (2021) · Oral Oncology · doi:10.1016/j.oraloncology.2021.105254 · Cited by 132
Alli, A. A., & Alam, M. M. (2019) · Internet of Things · doi:10.1016/j.iot.2019.100070 · Cited by 124
A novel autophagy‐related lncRNA prognostic risk model for breast cancer
Li, X., Jin, F., & Li, Y. (2020) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.15980 · Cited by 120
Skin Cancer Classification Using Deep Spiking Neural Network
Gilani, S. Q., Syed, T., Umair, M., & Marques, O. (2023) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-023-00776-2 · Cited by 127
Health Big Data Analytics: A Technology Survey
Harerimana, G., Jang, B., Kim, J. W., & Park, H. K. (2018) · IEEE Access · doi:10.1109/access.2018.2878254 · Cited by 119
Predicting breast cancer 5-year survival using machine learning: A systematic review
Li, J., Zhou, Z., Dong, J., Fu, Y., Li, Y., Luan, Z., & Peng, X. (2021) · PLoS ONE · doi:10.1371/journal.pone.0250370 · Cited by 128
A Machine Learning Method for Classification of Cervical Cancer
Tanimu, J. J., Hamada, M., Hassan, M., Kakudi, H. A., & Abiodun, O. J. (2022) · Electronics · doi:10.3390/electronics11030463 · Cited by 118
Microglial morphometric analysis: so many options, so little consistency
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Machine Learning for Survival Analysis: A SurveyPreprint
Wang, P., Li, Y., & Reddy, C. K. (2017) · arXiv.org · doi:10.48550/arxiv.1708.04649 · Cited by 452
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salman, K., & Sonuç, E. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1963/1/012140 · Cited by 117
Tsopra, R., Fernández, X. M., Luchinat, C., Alberghina, L., Lehrach, H., Vanoni, M., Dreher, F., Sezerman, O. U., Marc Cuggia, A., de Tayrac, M., et al. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01634-3 · Cited by 122
Chen, C.-W., Lin, M.-H., Liao, C., Chang, H., & Chu, Y.-W. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.02.021 · Cited by 116
Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning
Xu, W., Sun, N., Gao, H., Chen, Z., Yang, Y., Ju, B., & Tang, L. (2021) · Scientific Reports · doi:10.1038/s41598-021-82492-x · Cited by 116
Karadaghy, O. A., Shew, M. A., New, J., & Bur, A. M. (2019) · JAMA Otolaryngology–Head & Neck Surgery · doi:10.1001/jamaoto.2019.0981 · Cited by 120
Blueprint for cancer research: Critical gaps and opportunities
Elmore, L. W., Greer, S. F., Daniels, E. C., Saxe, C. C., Melner, M. H., Krawiec, G. M., Cance, W. G., & Phelps, W. C. (2020) · CA A Cancer Journal for Clinicians · doi:10.3322/caac.21652 · Cited by 124
Ogundokun, R. O., Misra, S., Douglas, M., Damaševičius, R., & Maskeliūnas, R. (2022) · Future Internet · doi:10.3390/fi14050153 · Cited by 114
Lung cancer prediction model using ensemble learning techniques and a systematic review analysis
Mamun, M., Farjana, A., Mamun, M. A., & Ahammed, M. S. (2022) · IEEE World AI IoT Congress (AIIoT) · doi:10.1109/aiiot54504.2022.9817326 · Cited by 114
Machine learning methodologies versus cardiovascular risk scores, in predicting disease risk
Dimopoulos, A. C., Νικολαϊδου, Μ., Caballero, F. F., Engchuan, W., Niubo, A. S., Arndt, H., Ayuso‐Mateos, J. L., Haro, J. M., Chatterji, S., Georgousopoulou, E. N., et al. (2018) · BMC Medical Research Methodology · doi:10.1186/s12874-018-0644-1 · Cited by 118
Dhiman, P., Ma, J., Navarro, C. L. A., Speich, B., Bullock, G. S., Damen, J. A., Kirtley, S., Hooft, L., Riley, R. D., CALSTER, B. V., et al. (2021) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2021.06.024 · Cited by 113
Transcriptomics and solid tumors: The next frontier in precision cancer medicine
Tsimberidou, A. M., Fountzilas, E., Bleris, L. G., & Kurzrock, R. (2020) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2020.09.007 · Cited by 118
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Castillo-Sánchez, G., Marques, G., Dorronzoro, E., Rivera-Romero, O., Franco, M., & Díez, I. D. L. T. (2020) · Journal of Medical Systems · doi:10.1007/s10916-020-01669-5 · Cited by 121
Bolourani, S., Brenner, M., Wang, P., McGinn, T. G., Hirsch, J. S., Barnaby, D. P., & Zanos, T. P. (2021) · Journal of Medical Internet Research · doi:10.2196/24246 · Cited by 111
A nonparametric updating method to correct clinical prediction model drift
Davis, S. E., Greevy, R., Fonnesbeck, C., Lasko, T., Walsh, C. G., & Matheny, M. E. (2019) · J. Am. Medical Informatics Assoc. · doi:10.1093/jamia/ocz127 · Cited by 112
Mishra, S., Thakkar, H. K., Mallick, P. K., Tiwari, P., & Alamri, A. M. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103079 · Cited by 110
Silva, K. D., Lee, W. K., Forbes, A. B., Demmer, R. T., Barton, C., & Enticott, J. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104268 · Cited by 108
State of Health Estimation Methods for Lithium-Ion Batteries
Nuroldayeva, G., Serik, Y., Adair, D., Uzakbaiuly, B., & Bakenov, Z. (2023) · International Journal of Energy Research · doi:10.1155/2023/4297545 · Cited by 109
Eloranta, S., Smedby, K. E. E., Dickman, P. W., & Andersson, T. (2020) · Journal of Internal Medicine · doi:10.1111/joim.13139 · Cited by 110
Papp, L., Pötsch, N., Grahovac, M., Schmidbauer, V. U., Wöehrer, A., Preusser, M., Mitterhauser, M., Kiesel, B., Wadsak, W., Beyer, T., et al. (2017) · Journal of Nuclear Medicine · doi:10.2967/jnumed.117.202267 · Cited by 106
Cao, Y., Fang, X. L., Ottosson, J., Näslund, E. B., & Stenberg, E. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8050668 · Cited by 106
López-García, G., Jerez, J. M., Franco, L., & Veredas, F. J. (2020) · PLoS ONE · doi:10.1371/journal.pone.0230536 · Cited by 110
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Metadata
- Authors
- Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I.
- Affiliations
- University of Ioannina (GR)
- National and Kapodistrian University of Athens (GR)
- Corresponding author
- Fotiadis, Dimitrios I.
- Venue
- Computational and Structural Biotechnology Journal
- Published
- 2014-11-15
- Volume
- 13
- Pages
- 8-17
- ISSN
- 2001-0370 (ISSN-L)
- PMC
- PMC4348437
- OpenAlex
- W2111547563
- Cited by
- 3,338
- References
- 72
- Citations by year
- 2026: 302 · 2025: 406 · 2024: 401 · 2023: 405 · 2022: 393 · 2021: 433 · 2020: 425 · 2019: 274 · 2018: 158 · 2017: 80 · 2016: 49 · 2015: 11 · 2014: 1
- FWCI
- 4.98
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computational and Structural Biotechnology Journal (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- funded by the project NEOMARK (FP7-ICT 224483)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- cancer prognosis, cancer risk prediction, prediction model, artificial neural networks, Bayesian networks, support vector machine, decision tree, supervised learning, clinical decision support, tumor heterogeneity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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