Skip to main content

2019 | OriginalPaper | Buchkapitel

A New Generalized Neuron Model Applied to DNA Microarray Classification

verfasst von : Beatriz A. Garro, Roberto A. Vazquez

Erschienen in: Engineering Applications of Neural Networks

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The DNA Microarray classification played an important role in bioinformatics and medicine area. By means of the genetic expressions obtained from a DNA microarrays, it is possible to identify which genes are correlated to a particular disease, in order solve different tasks such as tumor detection, best treatment selection, etc. In the last years, several computational intelligence techniques have been proposed to identify different groups of genes associated with a particular disease; one popular example is the application of artificial neural networks (ANN). The main disadvantage of using this technique is that ANN require a representative number of samples to provide acceptable results. However, the enormous quantity of genes and the few samples available for any disease, demand the use of more robust artificial neural models, capable of providing acceptable results using few samples during the learning process. In this research, we described a new type of generalized neuron model (GNM) applied to the DNA microarray classification task. The proposed methodology selects the set of genes that better describe the disease applying the artificial bee colony algorithm; after that, the GNM is trained using the discovered genes by means of a differential evolution algorithm. Finally, the accuracy of the proposed methodology is evaluated classifying two types of cancer using DNA microarrays: the acute lymphocytic leukemia and the acute myeloid leukemia.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Peterson, L., et al.: Artificial neural network analysis of DNA microarray-based prostate cancer recurrence. In: 2005 Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2005, pp. 1–8, November 2005 Peterson, L., et al.: Artificial neural network analysis of DNA microarray-based prostate cancer recurrence. In: 2005 Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2005, pp. 1–8, November 2005
2.
Zurück zum Zitat Huynh, H.T., Kim, J.J., Won, Y.: Classification study on DNA micro array with feed forward neural network trained by singular value decomposition. Int. J. Bio-Sci. Bio-Technol. 1, 17–24 (2009) Huynh, H.T., Kim, J.J., Won, Y.: Classification study on DNA micro array with feed forward neural network trained by singular value decomposition. Int. J. Bio-Sci. Bio-Technol. 1, 17–24 (2009)
3.
Zurück zum Zitat Khan, J., et al.: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat. Med. 7(6), 673–679 (2001)CrossRef Khan, J., et al.: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat. Med. 7(6), 673–679 (2001)CrossRef
4.
Zurück zum Zitat Lancashire, L.J., Lemetre, C., Ball, G.R.: An introduction to artificial neural networks in bioinformaticsapplication to complex microarray and mass spectrometry datasets in cancer studies. Briefings Bioinform. 10(3), 315–329 (2009)CrossRef Lancashire, L.J., Lemetre, C., Ball, G.R.: An introduction to artificial neural networks in bioinformaticsapplication to complex microarray and mass spectrometry datasets in cancer studies. Briefings Bioinform. 10(3), 315–329 (2009)CrossRef
5.
Zurück zum Zitat Chen, W., Lu, H., Wang, M., Fang, C.: Gene expression data classification using artificial neural network ensembles based on samples filtering. In: 2009 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009, vol. 1, pp. 626–628, November 2009 Chen, W., Lu, H., Wang, M., Fang, C.: Gene expression data classification using artificial neural network ensembles based on samples filtering. In: 2009 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009, vol. 1, pp. 626–628, November 2009
6.
Zurück zum Zitat Peterson, L.E., Coleman, M.A.: Comparison of gene identification based on artificial neural network pre-processing with k-means cluster and principal component analysis. In: Bloch, I., Petrosino, A., Tettamanzi, A.G.B. (eds.) WILF 2005. LNCS (LNAI), vol. 3849, pp. 267–276. Springer, Heidelberg (2006). https://doi.org/10.1007/11676935_33CrossRef Peterson, L.E., Coleman, M.A.: Comparison of gene identification based on artificial neural network pre-processing with k-means cluster and principal component analysis. In: Bloch, I., Petrosino, A., Tettamanzi, A.G.B. (eds.) WILF 2005. LNCS (LNAI), vol. 3849, pp. 267–276. Springer, Heidelberg (2006). https://​doi.​org/​10.​1007/​11676935_​33CrossRef
8.
Zurück zum Zitat Garro, B.A., Rodríguez, K., Vázquez, R.A.: Classification of DNA microarrays using artificial neural networks and ABC algorithm. Appl. Soft Comput. 38, 548–560 (2016)CrossRef Garro, B.A., Rodríguez, K., Vázquez, R.A.: Classification of DNA microarrays using artificial neural networks and ABC algorithm. Appl. Soft Comput. 38, 548–560 (2016)CrossRef
9.
Zurück zum Zitat Garro, B.A., Vázquez, R.A.: Designing artificial neural networks using particle swarm optimization algorithms. Comp. Int. and Neurosc. 2015, 369298:1–369298:20 (2015) Garro, B.A., Vázquez, R.A.: Designing artificial neural networks using particle swarm optimization algorithms. Comp. Int. and Neurosc. 2015, 369298:1–369298:20 (2015)
10.
Zurück zum Zitat Kulkarni, R.V., Venayagamoorthy, G.K.: Generalized neuron: feedforward and recurrent architectures. Neural Netw. 22(7), 1011–1017 (2009)CrossRef Kulkarni, R.V., Venayagamoorthy, G.K.: Generalized neuron: feedforward and recurrent architectures. Neural Netw. 22(7), 1011–1017 (2009)CrossRef
11.
Zurück zum Zitat Rizwan, M., Jamil, M., Kothari, D.: Generalized neural network approach for global solar energy estimation in India. IEEE Trans. Sustain. Energy 3(3), 576–584 (2012)CrossRef Rizwan, M., Jamil, M., Kothari, D.: Generalized neural network approach for global solar energy estimation in India. IEEE Trans. Sustain. Energy 3(3), 576–584 (2012)CrossRef
12.
Zurück zum Zitat Kiran, R., Venayagamoorthy, G.K., Palaniswami, M.: Density estimation using a generalized neuron. In: 9th International Conference on Information Fusion, FUSION 2006, Florence, Italy, 10–13 July 2006, pp. 1–7. IEEE (2006) Kiran, R., Venayagamoorthy, G.K., Palaniswami, M.: Density estimation using a generalized neuron. In: 9th International Conference on Information Fusion, FUSION 2006, Florence, Italy, 10–13 July 2006, pp. 1–7. IEEE (2006)
13.
Zurück zum Zitat Kiran, R., Jetti, S.R., Venayagamoorthy, G.K.: Online training of a generalized neuron with particle swarm optimization. In: Proceedings of the International Joint Conference on Neural Networks, IJCNN 2006, Part of the IEEE World Congress on Computational Intelligence, WCCI 2006, Vancouver, BC, Canada, 16–21 July 2006, pp. 5088–5095. IEEE (2006) Kiran, R., Jetti, S.R., Venayagamoorthy, G.K.: Online training of a generalized neuron with particle swarm optimization. In: Proceedings of the International Joint Conference on Neural Networks, IJCNN 2006, Part of the IEEE World Congress on Computational Intelligence, WCCI 2006, Vancouver, BC, Canada, 16–21 July 2006, pp. 5088–5095. IEEE (2006)
14.
Zurück zum Zitat Garro, B.A., Rodríguez, K., Vázquez, R.A.: Generalized neurons and its application in DNA microarray classification. In: IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, BC, Canada, 24–29 July 2016, pp. 3110–3115. IEEE (2016) Garro, B.A., Rodríguez, K., Vázquez, R.A.: Generalized neurons and its application in DNA microarray classification. In: IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, BC, Canada, 24–29 July 2016, pp. 3110–3115. IEEE (2016)
15.
Zurück zum Zitat George, G., Raimond, K.: Article: a survey on optimization algorithms for optimizing the numerical functions. Int. J. Comput. Appl. 61(6), 41–46 (2013). Full text available George, G., Raimond, K.: Article: a survey on optimization algorithms for optimizing the numerical functions. Int. J. Comput. Appl. 61(6), 41–46 (2013). Full text available
16.
Zurück zum Zitat Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report, Computer Engineering Department, Engineering Faculty, Erciyes University (2005) Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report, Computer Engineering Department, Engineering Faculty, Erciyes University (2005)
17.
Zurück zum Zitat Storn, R., Price, K.: Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report (1995) Storn, R., Price, K.: Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report (1995)
18.
Zurück zum Zitat Golub, T.R., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439), 531–537 (1999)CrossRef Golub, T.R., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439), 531–537 (1999)CrossRef
19.
Zurück zum Zitat Sahu, B., Mishra, D.: A novel feature selection algorithm using particle swarm optimization for cancer microarray data. Proc. Eng. 38(0), 27–31 (2012). International Conference on Modelling Optimization and Computing Sahu, B., Mishra, D.: A novel feature selection algorithm using particle swarm optimization for cancer microarray data. Proc. Eng. 38(0), 27–31 (2012). International Conference on Modelling Optimization and Computing
20.
Zurück zum Zitat Wang, A., An, N., Chen, G., Li, L., Alterovitz, G.: Improving PLSRFE based gene selection for microarray data classification. Comput. Biol. Med. 62, 14–24 (2015)CrossRef Wang, A., An, N., Chen, G., Li, L., Alterovitz, G.: Improving PLSRFE based gene selection for microarray data classification. Comput. Biol. Med. 62, 14–24 (2015)CrossRef
21.
Zurück zum Zitat Alshamlan, H.M., Badr, G.H., Alohali, Y.A.: Genetic bee colony (GBC) algorithm: a new gene selection method for microarray cancer classification. Comput. Biol. Chem. 56, 49–60 (2015)CrossRef Alshamlan, H.M., Badr, G.H., Alohali, Y.A.: Genetic bee colony (GBC) algorithm: a new gene selection method for microarray cancer classification. Comput. Biol. Chem. 56, 49–60 (2015)CrossRef
Metadaten
Titel
A New Generalized Neuron Model Applied to DNA Microarray Classification
verfasst von
Beatriz A. Garro
Roberto A. Vazquez
Copyright-Jahr
2019
DOI
https://doi.org/10.1007/978-3-030-20257-6_11