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Cancer prediction with gene expression profiling and differential evolution

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Abstract

In the field of bioinformatics, the classification of tumors is a difficult and time-consuming task. When diagnosing cancer, gene expression levels are typically one of the most useful tools. However, the biological noise present in microarray data leads to unsatisfactory precision and accuracy. The utilization of thousands of genes in the process of diagnosing tumors is an important task. The two levels of feature selection have been proposed in order to determine the genes that are the most informative to diagnose cancer. Using three different statistical methods, the first level of selection reveals the prognostic genes. In the second level, the differential evolution algorithm considers the prognostic genes that were obtained from statistical measures as initial members to identify the most relevant features. The scaling factor in the modified differential evolution algorithm was made to vary in a dynamic manner in order to evolve the mutant member of the population. The proposed model is a hybrid of statistical approach and evolutionary computation with modified differential evolution algorithm that identifies the candidate genes from thousands of genes from gene expression data. The findings obtained through this hybrid approach upon testing five gene expression datasets provide evidence that it has outperformed when compared to the existing systems for DLBCL outcome, prostate outcome, prostate, and colon tumor datasets with improved classification accuracies of 14%, 4%, 0.62%, and 0.13%, respectively.

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Availability of datasets

The gene expression datasets used in this work are available in Open Repositories.

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Funding

No funding was provided to carry out this work.

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Authors and Affiliations

  1. ECE, Mahatma Gandhi Institute of Technology, Gandipet, Hyderabad, 500075, Telangana, India

    T R Vijaya Lakshmi

  2. EEE, Chaitanya Bharathi Institute of Technology, Gandipet, Hyderabad, 500075, Telangana, India

    Ch. Venkata Krishna Reddy

Authors
  1. T R Vijaya Lakshmi
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  2. Ch. Venkata Krishna Reddy
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Contributions

T.R.Vijaya Lakshmi planned the experiments, designed the two level computational framework and took the lead in writing the manuscript. Ch.Venkata Krishna Reddy has carried out the simulations, aided in interpreting the results and worked on the proof outline.

Corresponding author

Correspondence to T R Vijaya Lakshmi.

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The authors have no relevant financial or non-financial interests to disclose. No funds, grants, or other support was received for conducting this study. The authors have no competing interests to declare that are relevant to the content of this article.

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Vijaya Lakshmi, T.R., Krishna Reddy, C.V. Cancer prediction with gene expression profiling and differential evolution. SIViP 17, 1855–1861 (2023). https://doi.org/10.1007/s11760-022-02396-9

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  • DOI: https://doi.org/10.1007/s11760-022-02396-9

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