Research Article
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Year 2021, Volume: 9 Issue: 1, 23 - 32, 30.01.2021
https://doi.org/10.17694/bajece.604885

Abstract

References

  • Loscalzo S., YU L., Ding C., “Consensus Group Stable Feature Selection”, June28–July1, Paris,France, 2009.
  • Loscalzo S., YU L., Ding C., “Stable Feature Selection via Dense Feature Groups”, August 24–27, Las Vegas, Nevada, USA, 2008.
  • Farhan M., Mohsin M., HamdanA.,Bakar A.A., “An evaluation of feature selection technique for dentrite cell algorithm”, IEEE, 2014.
  • Schmidhuber, J., Wierstra, D., and Gomez, F. J. Evolino: “Hybrid neuroevolution/optimal linear search for sequence prediction”. In Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI), pp. 853–858, 2005
  • Rubvurm M., Körner M., “Temporal vegetation modelling using long short-term memory networks for crop identification from medium-resolution multi-spectral satellite images”, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), USA, 2017.
  • Dasgupta D., Nino L.F., Immunological computation: theory and applications .2009 by Taylor & Francis Group, LLC. Book, 2009.
  • Chung Y., et al., “Emprical evaluation of gated recurrent neural networks on sequence modeling”, Neural ComputAppl arxiv:1412.3555v1, 2014.
  • Luque-Baena R.M., Urda D., Gonzalo Claros M., Franco L., and Jerez J.M., “Robust genesignatustes from microarray data using genetic algorithms encriched with biological pathway keywords”, Journal of Biomedical Informatics, http://dx.doi.org/10.1016/j.jbi.2014.01.006, 2014.
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  • Ferreira A.j. (2014), “Feature selection and discretization for high-dimensional data”, Phd Thesis.
  • Mazel J. (2011), “ Unsupervised network anomaly detection”, Phd Thesis.
  • Polat K., Güneş S., Sahan S., Kodaz H., (2005), “A New Classification Method for Breast Cancer Diagnosis: Feature Selection Artificial Immune Recognition System (FS-AIRS)”, Berlin.
  • Menon A. edited by. (2004).Frontiers of Evolutionary Computation, Kluwer academic Publishers, Pittsburgh, USA.
  • Oh S. , Lee J.S., Moon B.R.(2004).Hybrid Genetic Algorithms for Feature Selection .Vol. 26, No.11, November.
  • Brownlee J. (2005). “Clonal Selection Theory &Clonalg the Clonal Selectıon Classıfıcatıon Algorıthm (CSCA), Technical Report.
  • Dudek G. (2012). “An Artificial System for Classification with Local Feature Selection”,IEEE Transaction on Evolutionary Computation. December 2012,Czestochowa.
  • Wang K., Chen K.,Adrian A., (2014), “An improved artificial immune recognition system with the opposite sign test for feature selection”, Knowledge-Based Systems ,Taiwan.
  • Daga M., Lakhwani K., (2013),” A Novel Content Based Image Retrieval Implemented ByNsa of Aıs”, Internatıonal Journal of Scıentıfıc & Technology Research.
  • Farhan M., Mohsin M., HamdanA.,Bakar A.A., (2014),” An Evaluation of Feature Selection Technique for Dendrite Cell Algorithm”,IEEE.
  • Gu F., Greensmith J., Aickelin U., (2008),” Further Exploration of the Dendritic Cell Algorithm: Antigen Multiplier and Time Windows”, ICARIS 2008.
  • Wollmer M., Eyben F., Rigoll G., (2008),” Combining Long Short-Term Memory and Dynamic Bayesian Networks for Incremental Emotion-Sensitive Artificial Listening”, Journal of Selected Topıcs in Signal Processing.
  • Daga M., Lakhwani K., (2013), “A Novel Content Based Image Retrieval Implemented By NSA Of AIS”, International journal of scientific & technology research
  • Kalousis A., Prados J. and Hilario M., (2007),“Stability of Feature Selection Algorithms: a study on high-dimensional spaces”, Knowladge and Information Systems.
  • Saeys Y., Abeel T., and Peer Y.V. (2008),”Robust Feature Selection Using Ensemble Feature Selection Techniques”. In Proceedings of the ECML Conference, pages 313-325.

Sequential Feature Maps with LSTM Recurrent Neural Networks for Robust Tumor Classification

Year 2021, Volume: 9 Issue: 1, 23 - 32, 30.01.2021
https://doi.org/10.17694/bajece.604885

Abstract

In the field of biomedicine, applications of
the identification of biomarkers require a robust gene selection mechanism. For
identifying the characteristic marker of an observed event, the selection of
attributes becomes important. The robustness of gene selection methods affects
detecting biologically meaningful genes in tumor diagnosis. For mapping,
sequential features Long-short-term memory (LSTM) was used with Artificial
Immune Recognition Systems (AIRS) to remember gene sequences and effectively
recall of learned sequential patterns. An attempt was made to improve AIRS with
LSTM which is a type of RNNs to produce discriminative gene subsets for finding
biologically meaningful genes in tumor diagnosis. The algorithms were evaluated
using six common cancer microarray datasets. By converging to the intrinsic
information of the microarray datasets, specific groups like functions of the
co-regulated groups were observed. The results showed that the LSTM based AIRS
model can successfully identify biologically significant genes from the
microarray datasets. Also the predictive genes for biological sequences
areimportant in gene expression microarrays.This study confirms that different
genes can be found in the same pathways. It was also found that the gene
subsets selected by the algorithms were involved in important biological
pathways.In this work we try an LSTM on our learning problem. We suspected that
recurrent neural networks would be a good architecture for making
predictions.The results show that the optimal gene subsets were based on the
suggested framework, so they should have rich biomedical interpretability.

References

  • Loscalzo S., YU L., Ding C., “Consensus Group Stable Feature Selection”, June28–July1, Paris,France, 2009.
  • Loscalzo S., YU L., Ding C., “Stable Feature Selection via Dense Feature Groups”, August 24–27, Las Vegas, Nevada, USA, 2008.
  • Farhan M., Mohsin M., HamdanA.,Bakar A.A., “An evaluation of feature selection technique for dentrite cell algorithm”, IEEE, 2014.
  • Schmidhuber, J., Wierstra, D., and Gomez, F. J. Evolino: “Hybrid neuroevolution/optimal linear search for sequence prediction”. In Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI), pp. 853–858, 2005
  • Rubvurm M., Körner M., “Temporal vegetation modelling using long short-term memory networks for crop identification from medium-resolution multi-spectral satellite images”, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), USA, 2017.
  • Dasgupta D., Nino L.F., Immunological computation: theory and applications .2009 by Taylor & Francis Group, LLC. Book, 2009.
  • Chung Y., et al., “Emprical evaluation of gated recurrent neural networks on sequence modeling”, Neural ComputAppl arxiv:1412.3555v1, 2014.
  • Luque-Baena R.M., Urda D., Gonzalo Claros M., Franco L., and Jerez J.M., “Robust genesignatustes from microarray data using genetic algorithms encriched with biological pathway keywords”, Journal of Biomedical Informatics, http://dx.doi.org/10.1016/j.jbi.2014.01.006, 2014.
  • https://machinelearning-blog.com/2018/02/21/recurrent-neural-networks/
  • https://www.cs.waikato.ac.nz/ml/weka/
  • https://worldwidescience.org/topicpages/i/immunity-related+gtpase+irgml.html
  • https://www.genecards.org/cgiin/carddisp.pl?gene=PCBP4
  • http://www.bionewsonline.com/
  • Ferreira A.j. (2014), “Feature selection and discretization for high-dimensional data”, Phd Thesis.
  • Mazel J. (2011), “ Unsupervised network anomaly detection”, Phd Thesis.
  • Polat K., Güneş S., Sahan S., Kodaz H., (2005), “A New Classification Method for Breast Cancer Diagnosis: Feature Selection Artificial Immune Recognition System (FS-AIRS)”, Berlin.
  • Menon A. edited by. (2004).Frontiers of Evolutionary Computation, Kluwer academic Publishers, Pittsburgh, USA.
  • Oh S. , Lee J.S., Moon B.R.(2004).Hybrid Genetic Algorithms for Feature Selection .Vol. 26, No.11, November.
  • Brownlee J. (2005). “Clonal Selection Theory &Clonalg the Clonal Selectıon Classıfıcatıon Algorıthm (CSCA), Technical Report.
  • Dudek G. (2012). “An Artificial System for Classification with Local Feature Selection”,IEEE Transaction on Evolutionary Computation. December 2012,Czestochowa.
  • Wang K., Chen K.,Adrian A., (2014), “An improved artificial immune recognition system with the opposite sign test for feature selection”, Knowledge-Based Systems ,Taiwan.
  • Daga M., Lakhwani K., (2013),” A Novel Content Based Image Retrieval Implemented ByNsa of Aıs”, Internatıonal Journal of Scıentıfıc & Technology Research.
  • Farhan M., Mohsin M., HamdanA.,Bakar A.A., (2014),” An Evaluation of Feature Selection Technique for Dendrite Cell Algorithm”,IEEE.
  • Gu F., Greensmith J., Aickelin U., (2008),” Further Exploration of the Dendritic Cell Algorithm: Antigen Multiplier and Time Windows”, ICARIS 2008.
  • Wollmer M., Eyben F., Rigoll G., (2008),” Combining Long Short-Term Memory and Dynamic Bayesian Networks for Incremental Emotion-Sensitive Artificial Listening”, Journal of Selected Topıcs in Signal Processing.
  • Daga M., Lakhwani K., (2013), “A Novel Content Based Image Retrieval Implemented By NSA Of AIS”, International journal of scientific & technology research
  • Kalousis A., Prados J. and Hilario M., (2007),“Stability of Feature Selection Algorithms: a study on high-dimensional spaces”, Knowladge and Information Systems.
  • Saeys Y., Abeel T., and Peer Y.V. (2008),”Robust Feature Selection Using Ensemble Feature Selection Techniques”. In Proceedings of the ECML Conference, pages 313-325.
There are 28 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence
Journal Section Araştırma Articlessi
Authors

Canan Batur Şahin 0000-0002-2131-6368

Banu Diri 0000-0002-2131-6368

Publication Date January 30, 2021
Published in Issue Year 2021 Volume: 9 Issue: 1

Cite

APA Batur Şahin, C., & Diri, B. (2021). Sequential Feature Maps with LSTM Recurrent Neural Networks for Robust Tumor Classification. Balkan Journal of Electrical and Computer Engineering, 9(1), 23-32. https://doi.org/10.17694/bajece.604885

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