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Statistical Learning Analysis of Thyroid Cancer Microarray Data

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Bioinformatics and Biomedical Engineering (IWBBIO 2022)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13347))

Abstract

The classification of human cancers constitutes to date a significant challenge in the context of microarray data analysis. The discovery of gene hallmarks for biological processes involves the examination of large gene expression matrices in a broad and massively parallel manner. In this article, a comprehensive and comparative analysis of thyroid cancer datasets is presented, including stages for feature selection, hypothesis testing, and classification. Also, datasets are integrated, and results for this integration are reported and analyzed. To conclude, text mining is used to investigate some biological information regarding the main resulting characteristic genes. Some genes found during the research, HINT3 in particular, appear to be worth to be further studied.

Supported by CONICET (112-2017-0100829) and SGCyT-UNS (24/N052).

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Author information

Authors and Affiliations

  1. Institute for Computer Science and Engineering (UNS-CONICET), Bahía Blanca, Argentina

    Rocío L. Cecchini, Ignacio Ponzoni & Jessica A. Carballido

  2. Department of Computer Science and Engineering, Universidad Nacional del Sur, Bahía Blanca, Argentina

    Iván Petrini, Rocío L. Cecchini, Ignacio Ponzoni & Jessica A. Carballido

  3. Cancer Biology Laboratory (UNS-CONICET), Universidad Nacional del Sur, Bahía Blanca, Argentina

    Marilina Mascaró

Authors
  1. Iván Petrini
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  2. Rocío L. Cecchini
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  3. Marilina Mascaró
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  4. Ignacio Ponzoni
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  5. Jessica A. Carballido
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Corresponding author

Correspondence to Ignacio Ponzoni .

Editor information

Editors and Affiliations

  1. Marcelina Siebold Guest Relations Dept., University of Granada, Granada, Spain

    Ignacio Rojas

  2. Faculty of Sciences, University of Granada, Granada, Spain

    Olga Valenzuela

  3. ETSIIT. CITIC-UGR, University of Granada, Granada, Spain

    Fernando Rojas

  4. ETSIIT, University of Granada, Granada, Spain

    Luis Javier Herrera

  5. University of Granada, Granada, Spain

    Francisco Ortuño

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Petrini, I., Cecchini, R.L., Mascaró, M., Ponzoni, I., Carballido, J.A. (2022). Statistical Learning Analysis of Thyroid Cancer Microarray Data. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2022. Lecture Notes in Computer Science(), vol 13347. Springer, Cham. https://doi.org/10.1007/978-3-031-07802-6_8

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  • DOI: https://doi.org/10.1007/978-3-031-07802-6_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07801-9

  • Online ISBN: 978-3-031-07802-6

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