Improving classification accuracy of cancer types using parallel hybrid feature selection on microarray gene expression data

Author:

Venkataramana LokeswariORCID,Jacob Shomona Gracia,Ramadoss Rajavel,Saisuma Dodda,Haritha Dommaraju,Manoja Kunthipuram

Funder

Science and Engineering Research Board

Publisher

Springer Science and Business Media LLC

Subject

Genetics,Molecular Biology,Biochemistry

Reference30 articles.

1. Ali SI, Shahzad W (2012) A feature subset selection method based on symmetric uncertainty and ant colony optimization. In: IEEE international conference on technologies (ICET), pp 1–6

2. Alshamlan HM, Badr GH, Alohali Y (2013) A study of cancer microarray gene expression prole: objectives and approaches. In: Proceedings of the world congress on engineering, vol 2, pp 1–6

3. Bang MS, Kang K, Lee JJ, Lee YJ, Choi JE, Ban JY, Oh CH (2017) Transcriptome analysis of non-small cell lung cancer and genetically matched adjacent normal tissues identifies novel prognostic marker genes. Genes Genom 39(3):277–284

4. Bioinformatics Laboratory (2019). http://www.biolab.si/supp/bi-ancer/projections/info/ALLGSE412_poterapiji.html . Accessed 20 July 2019

5. Bolón-CanedoV V, Sánchez-Maroño N, Alonso-Betanzos A (2015) Distributed feature selection: an application to microarray data classification. Appl Soft Comput 30:136–150

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