Machine learning applications in cancer prognosis and prediction

被引:1797
作者
Kourou, Konstantina [1 ]
Exarchos, Themis P. [1 ,2 ]
Exarchos, Konstantinos P. [1 ]
Karamouzis, Michalis V. [3 ]
Fotiadis, Dimitrios I. [1 ,2 ]
机构
[1] Univ Ioannina, Dept Mat Sci & Engn, Unit Med Technol & Intelligent Informat Syst, GR-45110 Ioannina, Greece
[2] IMBB FORTH, Dept Biomed Res, Ioannina, Greece
[3] Univ Athens, Sch Med, Dept Biol Chem, Mol Oncol Unit, GR-11527 Athens, Greece
关键词
Machine learning; Cancer susceptibility; Predictive models; Cancer recurrence; Cancer survival; BREAST-CANCER; NEURAL-NETWORKS; OUTCOME PREDICTION; FEATURE-SELECTION; DECISION-SUPPORT; RECURRENCE; MODEL; SUSCEPTIBILITY; SURVIVABILITY; MICROARRAY;
D O I
10.1016/j.csbj.2014.11.005
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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. (C) 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/3.0/).
引用
收藏
页码:8 / 17
页数:10
相关论文
共 72 条
[1]  
Adams S., 2012, IS COURSERA BEGINNIN
[2]   Support vector machines combined with feature selection for breast cancer diagnosis [J].
Akay, Mehmet Fatih .
EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (02) :3240-3247
[3]  
[Anonymous], MED APPL ARTIF INTEL
[4]  
[Anonymous], NUCL ACIDS RES
[5]   Breast Cancer Risk Estimation With Artificial Neural Networks Revisited Discrimination and Calibration [J].
Ayer, Turgay ;
Alagoz, Oguzhan ;
Chhatwal, Jagpreet ;
Shavlik, Jude W. ;
Kahn, Charles E., Jr. ;
Burnside, Elizabeth S. .
CANCER, 2010, 116 (14) :3310-3321
[6]   Variations in lung cancer risk among smokers [J].
Bach, PB ;
Kattan, MW ;
Thornquist, MD ;
Kris, MG ;
Tate, RC ;
Barnett, MJ ;
Hsieh, LJ ;
Begg, CB .
JNCI-JOURNAL OF THE NATIONAL CANCER INSTITUTE, 2003, 95 (06) :470-478
[7]   NCBI GEO: mining tens of millions of expression profiles - database and tools update [J].
Barrett, Tanya ;
Troup, Dennis B. ;
Wilhite, Stephen E. ;
Ledoux, Pierre ;
Rudnev, Dmitry ;
Evangelista, Carlos ;
Kim, Irene F. ;
Soboleva, Alexandra ;
Tomashevsky, Maxim ;
Edgar, Ron .
NUCLEIC ACIDS RESEARCH, 2007, 35 :D760-D765
[8]  
Bian X, 2009, DATA SUBMISSION CURA
[9]   Improving Breast Cancer Survival Analysis through Competition-Based Multidimensional Modeling [J].
Bilal, Erhan ;
Dutkowski, Janusz ;
Guinney, Justin ;
Jang, In Sock ;
Logsdon, Benjamin A. ;
Pandey, Gaurav ;
Sauerwine, Benjamin A. ;
Shimoni, Yishai ;
Vollan, Hans Kristian Moen ;
Mecham, Brigham H. ;
Rueda, Oscar M. ;
Tost, Jorg ;
Curtis, Christina ;
Alvarez, Mariano J. ;
Kristensen, Vessela N. ;
Aparicio, Samuel ;
Borresen-Dale, Anne-Lise ;
Caldas, Carlos ;
Califano, Andrea ;
Friend, Stephen H. ;
Ideker, Trey ;
Schadt, Eric E. ;
Stolovitzky, Gustavo A. ;
Margolin, Adam A. .
PLOS COMPUTATIONAL BIOLOGY, 2013, 9 (05)
[10]  
Bishop Christopher, 2006, Pattern Recognition and Machine Learning, DOI 10.1117/1.2819119