Machine learning applications in cancer prognosis and prediction

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 pat...

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Veröffentlicht in:	Computational and structural biotechnology journal Jg. 13; H. C; S. 8 - 17
Hauptverfasser:	Kourou, Konstantina, Exarchos, Themis P., Exarchos, Konstantinos P., Karamouzis, Michalis V., Fotiadis, Dimitrios I.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Netherlands Research Network of Computational and Structural Biotechnology 01.01.2015 Elsevier
Schlagworte:	at-risk population Bayesian theory bioinformatics biotechnology Cancer recurrence Cancer survival Cancer susceptibility data collection decision making decision support systems disease course early diagnosis Machine learning Mini Review neoplasms neural networks patients prediction Predictive models prognosis risk support vector machines SVM, Support Vector Machine ML, Machine Learning HTT, High-throughput Technologies Predictive models NSCLC, Non-small Cell Lung Cancer CFS, Correlation based Feature Selection TCGA, The Cancer Genome Atlas Research Network SSL, Semi-supervised Learning LCS, Learning Classifying Systems BN, Bayesian Network OSCC, Oral Squamous Cell Carcinoma Cancer recurrence ES, Early Stopping algorithm AUC, Area Under Curve BCRSVM, Breast Cancer Support Vector Machine ANN, Artificial Neural Network Cancer susceptibility GEO, Gene Expression Omnibus NCI caArray, National Cancer Institute Array Data Management System ROC, Receiver Operating Characteristic DT, Decision Tree Cancer survival SEER, Surveillance, Epidemiology and End results Database Machine learning PPI, Protein–Protein Interaction
ISSN:	2001-0370, 2001-0370
Online-Zugang:	Volltext
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