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Machine learning applications in cancer prognosis and prediction See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/269288435 Machine learning applications in cancer prognosis and prediction Article in Computational and Structural Biotechnology Journal · November 2014 DOI: 10.1016/j.csbj.2014.11.005 CITATIONS 2,347 READS 5,659 5 authors, including: Konstantina Kourou University of Ioannina 39 PUBLICATIONS 2,675 CITATIONS SEE PROFILE Themis Exarchos Ionian University 191 PUBLICATIONS 4,407 CITATIONS SEE PROFILE Konstantinos P Exarchos University of Ioannina 23 PUBLICATIONS 2,670 CITATIONS SEE PROFILE Michalis V Karamouzis National and Kapodistrian University of Athens 209 PUBLICATIONS 10,409 CITATIONS SEE PROFILE All content following this page was uploaded by Konstantina Kourou on 27 May 2015. 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https://www.researchgate.net/profile/Konstantina-Kourou?enrichId=rgreq-dbe3c2bb5408d0d7e667d7ff5ba35499-XXX&enrichSource=Y292ZXJQYWdlOzI2OTI4ODQzNTtBUzoyMzM2OTQxNzg4MzY0ODBAMTQzMjcyODQ0Mjk3MQ%3D%3D&el=1_x_10&_esc=publicationCoverPdf 1 2 3Q1Q4 4 5 6 7 8 9 10 30 313233 3435 36 37 38 39 40 41 42 43 44 45 46 Computational and Structural Biotechnology Journal xxx (2014) xxx–xxx CSBJ-00044; No of Pages 10 Contents lists available at ScienceDirect journa l homepage: www.e lsev ie r .com/ locate /csb j Mini Review Machine learning applications in cancer prognosis and prediction O F Konstantina Kourou a, Themis P. Exarchos a,b, Konstantinos P. Exarchos a, Michalis V. Karamouzis c, Dimitrios I. Fotiadis a,b,⁎ a Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece b IMBB— FORTH, Dept. of Biomedical Research, Ioannina, Greece c Molecular Oncology Unit, Department of Biological Chemistry, Medical School, University of Athens, Athens, Greece Abbreviations:ML,MachineLearning;ANN,ArtificialNe CancerGenomeAtlas ResearchNetwork;HTT,High-throug ReceiverOperatingCharacteristic; BCRSVM,Breast Cancer Early Stopping algorithm; SEER, Surveillance, Epidemiolog System. ⁎ Corresponding author at: Unit of Medical Technology E-mail addresses:
[email protected] (M.V. Karamouzis),
[email protected] (D.I. Fotiadis). http://dx.doi.org/10.1016/j.csbj.2014.11.005 2001-0370/© 2014 Kourou et al. Published by Elsevier B.V license (http://creativecommons.org/licenses/by/4.0/). Please cite this article as: Kourou K, et al, Ma http://dx.doi.org/10.1016/j.csbj.2014.11.005 Oa b s t r a c t a r t i c l e i n f o 12 Available online xxxx 11 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 E C T E D P RCancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diag-nosis and prognosis of a cancer typehavebecomea 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 decisionmaking. 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 thesemethods to be considered in the everyday clinical prac- tice. In this work, we present a review of recentML approaches employed in themodeling of cancer progression. The predictivemodels discussed here are based on various supervisedML techniques aswell as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here themost recent publications that employ these techniques as an aim tomodel cancer risk or patient outcomes. © 2014 Kourou et al. Published by Elsevier B.V. on behalf of the Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). R Contents U N C O R 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0 2. ML techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0 3. ML and cancer prediction/prognosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0 4. Survey of ML applications in cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0 4.1. Prediction of cancer susceptibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0 4.2. Prediction of cancer recurrence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0 4.3. Prediction of cancer survival . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0 5. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0 6. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 0 uralNetwork;SVM,SupportVectorMachine;DT,DecisionTree;BN,BayesianNetwork;SSL,Semi-supervisedLearning;TCGA,The hput Technologies; OSCC,Oral SquamousCell Carcinoma;CFS, Correlation based FeatureSelection;AUC,AreaUnder Curve; ROC, Support VectorMachine; PPI, Protein–Protein Interaction;GEO,GeneExpressionOmnibus; LCS, LearningClassifyingSystems; ES, y and End results Database; NSCLC, Non-small Cell Lung Cancer; NCI caArray, National Cancer Institute Array DataManagement and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece. (K. Kourou),
[email protected] (T.P. Exarchos),
[email protected] (K.P. Exarchos),
[email protected] . on behalf of theResearchNetwork of Computational and Structural Biotechnology. This is an open access article under theCCBY chine learning applications in cancer prognosis and prediction, Comput Struct Biotechnol J (2014), http://creativecommons.org/licenses/by/4.0/ http://dx.doi.org/10.1016/j.csbj.2014.11.005 mailto:
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[email protected] http://dx.doi.org/10.1016/j.csbj.2014.11.005 http://creativecommons.org/licenses/by/4.0/ http://www.sciencedirect.com/science/journal/18077 www.elsevier.com/locate/csbj http://dx.doi.org/10.1016/j.csbj.2014.11.005 T 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 Fig. 1.Classification task in supervised learning. Tumors are represented as X and classified as benign or malignant. The circled examples depict those tumors that have been misclassified. 2 K. Kourou et al. / Computational and Structural Biotechnology Journal xxx (2014) xxx–xxx U N C O R R E C 1. Introduction Over the past decades, a continuous evolution related to cancer re- search has been performed [1]. Scientists applied different methods, such as screening in early stage, in order to find types of cancer before they cause symptoms. Moreover, they have developed new strategies for the early prediction of cancer treatment outcome. With the advent of new technologies in the field of medicine, large amounts of cancer data have been collected and are available to the medical research community. However, the accurate prediction of a disease outcome is one of themost interesting and challenging tasks for physicians. As a re- sult, ML methods have become a popular tool for medical researchers. These techniques can discover and identify patterns and relationships between them, from complex datasets,