Data mining methods for gene selection on the basis of gene expression arrays
Michał Muszyński, Stanisław Osowski (2014)
International Journal of Applied Mathematics and Computer Science
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Michał Muszyński, Stanisław Osowski (2014)
International Journal of Applied Mathematics and Computer Science
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Emily, Mathieu, Morel, Didier, Marcelpoil, Raphael, François, Olivier (2005)
Journal of Theoretical Medicine
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Pitt-Francis, Joe, Chen, Dan, Slaymaker, Mark, Simpson, Andre, Brady, Michael, van Leeuwen, Ingeborg, Reddington, Fiona, Quirke, Phil, Brown, Gina, Gavaghan, David (2006)
Computational & Mathematical Methods in Medicine
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Marek Kowal, Paweł Filipczuk (2014)
International Journal of Applied Mathematics and Computer Science
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Marek Kowal, Paweł Filipczuk (2014)
International Journal of Applied Mathematics and Computer Science
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Thomas Fevens, Adam Krzyżak (2008)
International Journal of Applied Mathematics and Computer Science
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According to the World Health Organization (WHO), breast cancer (BC) is one of the most deadly cancers diagnosed among middle-aged women. Precise diagnosis and prognosis are crucial to reduce the high death rate. In this paper we present a framework for automatic malignancy grading of fine needle aspiration biopsy tissue. The malignancy grade is one of the most important factors taken into consideration during the prediction of cancer behavior after the treatment. Our framework is based...
Bill, Jo, Fokoue, Ernest (2014)
Serdica Journal of Computing
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This research evaluates pattern recognition techniques on a subclass of big data where the dimensionality of the input space (p) is much larger than the number of observations (n). Specifically, we evaluate massive gene expression microarray cancer data where the ratio κ is less than one. We explore the statistical and computational challenges inherent in these high dimensional low sample size (HDLSS) problems and present statistical machine learning methods used to tackle and circumvent...
Mou'ath Hourani, Ibrahiem M. M. El Emary (2009)
Computer Science and Information Systems
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Kuznetsova, A.V., Sen'ko, O.V., Matchak, G.N., Vakhotsky, V.V., Zabotina, T.N., Korotkova, O.V. (2000)
Journal of Theoretical Medicine
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Joglekar, Prafulla, Chung, Q. B., Tavana, Madjid (2001)
Journal of Applied Mathematics and Decision Sciences
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Klinge, U., Dahl, E., Mertens, P.R. (2007)
Computational & Mathematical Methods in Medicine
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Marek Kimmel, Olga Gorlova (2003)
International Journal of Applied Mathematics and Computer Science
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A construction of a realistic statistical model of lung cancer risk and progression is proposed. The essential elements of the model are genetic and behavioral determinants of susceptibility, progression of the disease from precursor lesions through early (localized) tumors to disseminated disease, detection by various modalities, and medical intervention. Using model estimates as a foundation, mortality reduction caused by early-detection and intervention programs can be predicted under...
Dorsaf Zekri, Bruno Defude, Thierry Delot (2010)
RAIRO - Operations Research - Recherche Opérationnelle
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This article focuses on data aggregation in vehicular networks. In such networks, sensor data are usually produced and exchanged between vehicles in order to warn or inform the drivers when an event is detected (, ). In the following, we present a solution to aggregate and store these data in order to have a history of past events. We therefore use Flajolet-Martin sketches. Our goal is to generate additional knowledge to assist drivers by providing them useful information even if no...