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Open AccessArticle

Machine Learning-Based Ensemble Recursive Feature Selection of Circulating miRNAs for Cancer Tumor Classification

1
Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Universiteitsweg 99, 3584 CG Utrecht, The Netherlands
2
Nuevo Hospital Civil de Guadalajara “Dr. Juan I. Menchaca”, Salvador Quevedo y Zubieta 750, Independencia Oriente, Guadalajara C.P. 44340, Jalisco, Mexico
3
Laboratorio de Modelado Molecular, Bioinformática y Diseno de farmacos, Seccion de Estudios de Posgrado e Investigación, Escuela Superior de Medicina, Instituto Politécnico Nacional, Mexico City 11340, Mexico
4
Life Sciences and Health, Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands
5
Genome Data Science, Faculty of Technology, Bielefeld University, Universitätsstraße 25, 33615 Bielefeld, Germany
6
Global Centre of Excellence Immunology Danone Nutricia Research, Uppsalaan 12, 3584 CT Utrecht, The Netherlands
7
UMR 518 MIA-Paris, INRAE, Université Paris-Saclay, 75013 Paris, France
*
Author to whom correspondence should be addressed.
Cancers 2020, 12(7), 1785; https://doi.org/10.3390/cancers12071785
Received: 3 June 2020 / Revised: 25 June 2020 / Accepted: 29 June 2020 / Published: 3 July 2020
(This article belongs to the Collection Regulatory and Non-Coding RNAs in Cancer Epigenetic Mechanisms)
Circulating microRNAs (miRNA) are small noncoding RNA molecules that can be detected in bodily fluids without the need for major invasive procedures on patients. miRNAs have shown great promise as biomarkers for tumors to both assess their presence and to predict their type and subtype. Recently, thanks to the availability of miRNAs datasets, machine learning techniques have been successfully applied to tumor classification. The results, however, are difficult to assess and interpret by medical experts because the algorithms exploit information from thousands of miRNAs. In this work, we propose a novel technique that aims at reducing the necessary information to the smallest possible set of circulating miRNAs. The dimensionality reduction achieved reflects a very important first step in a potential, clinically actionable, circulating miRNA-based precision medicine pipeline. While it is currently under discussion whether this first step can be taken, we demonstrate here that it is possible to perform classification tasks by exploiting a recursive feature elimination procedure that integrates a heterogeneous ensemble of high-quality, state-of-the-art classifiers on circulating miRNAs. Heterogeneous ensembles can compensate inherent biases of classifiers by using different classification algorithms. Selecting features then further eliminates biases emerging from using data from different studies or batches, yielding more robust and reliable outcomes. The proposed approach is first tested on a tumor classification problem in order to separate 10 different types of cancer, with samples collected over 10 different clinical trials, and later is assessed on a cancer subtype classification task, with the aim to distinguish triple negative breast cancer from other subtypes of breast cancer. Overall, the presented methodology proves to be effective and compares favorably to other state-of-the-art feature selection methods. View Full-Text
Keywords: miRNAs; TNBC; machine learning; feature selection; circulating miRNAs; TNBC; machine learning; feature selection; circulating
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Lopez-Rincon, A.; Mendoza-Maldonado, L.; Martinez-Archundia, M.; Schönhuth, A.; Kraneveld, A.D.; Garssen, J.; Tonda, A. Machine Learning-Based Ensemble Recursive Feature Selection of Circulating miRNAs for Cancer Tumor Classification. Cancers 2020, 12, 1785.

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