Szczegóły

Tytuł artykułu

Fusion of feature selection methods in gene recognition

Tytuł czasopisma

Bulletin of the Polish Academy of Sciences: Technical Sciences

Rocznik

2021

Wolumin

69

Numer

3

Afiliacje

Gil, Fabian : Warsaw University of Technology, Pl. Politechniki 1, 00-661 Warsaw, Poland ; Osowski, Stanislaw : Warsaw University of Technology, Pl. Politechniki 1, 00-661 Warsaw, Poland ; Osowski, Stanislaw : Military University of Technology, ul. gen. Sylwestra Kaliskiego 2, 00-908 Warsaw, Poland

Autorzy

Słowa kluczowe

diagnostic features ; selection methods ; genes ; recognition ; biomarkers

Wydział PAN

Nauki Techniczne

Zakres

e136748

Bibliografia

  1.  I. Guyon and A. Elisseeff, “An introduction to variable and feature selection”, J. Mach. Learn. Res. 3, 1158–1182 (2003).
  2.  I. Guyon, A.J. Weston, S. Barnhill, and V. Vapnik, “Gene selection for cancer classification using SVM”, Mach. Learn. 46, 389‒422 (2003).
  3.  P.N. Tan, M. Steinbach, and V Kumar, Introduction to data mining, Boston, Pearson Education Inc., 2006.
  4.  H. Chen, Y. Zhang, and I. Gutman, “A kernel-based clustering method for gene selection with gene expression data”, J. Biomed. Inf orm. 62, 12‒20 (2016).
  5.  P. Das, A. Roychowdhury, S. Das, S. Roychoudhury, and S. Tripathy, “sigFeature: novel significant feature selection method for classification of gene expression data using support vector machine and t statistic”, Front. Genet. 11, 247 (2020), doi: 10.3389/fgene.2020.00247.
  6.  A. Wiliński and S. Osowski, “Ensemble of data mining methods for gene ranking”, Bull. Pol. Acad. Sci. Tech. Sci. 60, 461‒471 (2012).
  7.  H. Mitsubayashi, S. Aso, T. Nagashima, and Y. Okada, “Accurate and robust gene selection for disease classification using simple statistics, Biomed. Inf orm. 391, 68–71 (2008).
  8.  J. Xu, Y. Wang, K. Xu, and T. Zhang, “Feature genes selection using fuzzy rough uncertainty metric for tumour diagnosis”, Comput. Math. Method Med. 2019, 6705648 (2019), doi: 10.1155/2019/6705648.
  9.  B. Lyu and A. Haque, “Deep learning based tumour type classification using gene expression data”, bioRxiv, p. 364323 (2018), doi: 10.1101/364323.
  10.  F. Yang, “Robust feature selection for microarray data based on multi criterion fusion”, IEEE Trans. Comput. Biol. Bioinf . 8(4), 1080–1092 (2011).
  11.  M. Muszyński and S. Osowski, “Data mining methods for gene selection on the basis of gene expression arrays”, Int. J. .Appl. Math. Comput. Sci. 24(3), 657‒668 (2014).
  12.  T. Latkowski and S. Osowski, “Data mining for feature selection in gene expression autism data”, Expert Syst. Appl. 42(2), 864‒872 (2015).
  13.  Matlab user manual. Natick (USA): MathWorks: (2020).
  14.  P. Sprent, and N.C. Smeeton, Applied Nonparametric Statistical Methods. Boca Raton, Chapman & Hall/CRC, 2007.
  15.  R.O. Duda, P.E. Hart, and P. Stork, Pattern Classif ication and Scene Analysis, New York: Wiley, 2003.
  16.  Exxact. [Online]. https://blog.exxactcorp.com/scikitlearn-vs-mlr-for-machine-learning/
  17.  Tutorialspoint. [Online]. https://www.tutorialspoint.com/weka/weka_feature_selection.htm
  18.  R. Robnik-Sikonja, and I. Kononenko, “Theoretical and empirical analysis of Relief ”, Mach. Learn. 53, 23‒69 (2003).
  19.  W. Yang, K. Wang, and W. Zuo. “Neighborhood Component Feature Selection for High-Dimensional Data”, J. Comput. 7(1), 161‒168 (2012).
  20.  L. Breiman, “Random forests”, Mach. Learn. 45, 5–32 (2001).
  21.  NCBI database. [Online]. http://www.ncbi.nlm.nih.gov/sites/GDSbrowser?acc=GDS4431, (2011).
  22. http://discover1.mc.vanderbilt.edu/discover/public/mcsvm/
  23. http://sdmc.lit.org.sg/GEDatasets/Datasets.html
  24.  F. Gil and S. Osowski, “Feature selection methods in gene recognition problem”, in Proc. on-line Conf erence Computatational Methods in Electrical Engineering, 2020, pp. 1‒4.

Data

10.03.2021

Typ

Article

Identyfikator

DOI: 10.24425/bpasts.2021.136748 ; ISSN 2300-1917

Źródło

Bulletin of the Polish Academy of Sciences: Technical Sciences; Early Access; e136748
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