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Integration of Clinico-Pathological and microRNA Data for Intelligent Breast Cancer Relapse Prediction Systems

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Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2013)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 8452))

Abstract

This paper investigates the integration of clinico-pathological and microRNA data for breast cancer relapse prediction. Clinical and pathological data proved to be relevant in making predictions about cancer disease outcome. The most accurate predictive models can be obtained by using clinico-pathological information together with genomic information. We analyzed the performance of various combinations between twenty classification algorithms and thirteen feature selection methods. The best performer was the regularized regression method Elastic Net, using its built-in feature selection method, on the data set integrating clinico-pathological data with microRNAs. The hybrid signature contains four clinico-pathological features and fifteen microRNAs. We also evaluated the influence of the separation of patients according to ER status and the impact of the exclusion from the data set of HS molecules (novel microRNAs without an assigned miRBase ID) on the overall performance. Functional analysis of the microRNAs of the best classifier showed that they are involved in cancer related processes.

This project has been conducted through the program Partnerships in priority areas - PN II, developed with the support of ANCS, CNDI - UEFISCDI, project no. PN-II-PT-CACM-2011-3.1-1221.

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Notes

  1. 1.

    Componentwise Boosting, Diagonal Discriminant Analysis, Elastic Net, Fisher Discriminant Analysis, Tree-based Boosting, k-nearest neighbors, Linear Discriminant Analysis, Lasso, Feed-Forward Neural Networks, Probabilistic nearest neighbors, Penalized Logistic Regression, Partial Least Squares with Linear Discriminant Analysis, Partial Least Squares with logistic regression, Partial Least Squares with Random Forest, Probabilistic Neural Networks, Quadratic Discriminant Analysis, Random Forest, PAM, Shrinkage Discriminant Analysis, Support Vector Machine.

  2. 2.

    t test, Welch test, Wilcox test, F test, Kruskal-Wallis test, moderated t and F test (limma), One-step Recursive Feature Elimination, random forest variable importance measure, Lasso, Elastic Net, componentwise boosting, Golub ad-hoc criterium, shrinkcat.

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Author information

Authors and Affiliations

  1. SAIA & OncoPredict, Cluj-Napoca, Romania

    Adriana Birlutiu, Denisa Ardevan, Paul Bulzu, Camelia Pintea & Alexandru Floares

  2. “1 Decembrie 1918” University, Alba-Iulia, Romania

    Adriana Birlutiu

  3. Tech Univ Cluj-Napoca, North Univ Center Baia Mare, Baia Mare, Romania

    Camelia Pintea

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  1. Adriana Birlutiu
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  2. Denisa Ardevan
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  4. Camelia Pintea
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  5. Alexandru Floares
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Correspondence to Camelia Pintea .

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Editors and Affiliations

  1. University Nice Sophia Antipolis, Sophia Antipolis, France

    Enrico Formenti

  2. University of Salerno, Fisciano, Italy

    Roberto Tagliaferri

  3. University of Groningen, AG Groningen, The Netherlands

    Ernst Wit

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Birlutiu, A., Ardevan, D., Bulzu, P., Pintea, C., Floares, A. (2014). Integration of Clinico-Pathological and microRNA Data for Intelligent Breast Cancer Relapse Prediction Systems. In: Formenti, E., Tagliaferri, R., Wit, E. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2013. Lecture Notes in Computer Science(), vol 8452. Springer, Cham. https://doi.org/10.1007/978-3-319-09042-9_13

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  • DOI: https://doi.org/10.1007/978-3-319-09042-9_13

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