Next Article in Journal
Male Sexual Function after Allogeneic Hematopoietic Stem Cell Transplantation in Childhood: A Multicenter Study
Next Article in Special Issue
NCL Inhibition Exerts Antineoplastic Effects against Prostate Cancer Cells by Modulating Oncogenic MicroRNAs
Previous Article in Journal
Comment on McCarthy, C.; et al. Developing Picornaviruses for Cancer Therapy. Cancers 2019, 11, 685
Previous Article in Special Issue
Tumor-Suppressive miR-192-5p Has Prognostic Value in Pancreatic Ductal Adenocarcinoma
Open AccessArticle

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

Division of Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Universiteitsweg 99, 3584 CG Utrecht, The Netherlands
Nuevo Hospital Civil de Guadalajara “Dr. Juan I. Menchaca”, Salvador Quevedo y Zubieta 750, Independencia Oriente, Guadalajara C.P. 44340, Jalisco, Mexico
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
Life Sciences and Health, Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands
Genome Data Science, Faculty of Technology, Bielefeld University, Universitätsstraße 25, 33615 Bielefeld, Germany
Global Centre of Excellence Immunology Danone Nutricia Research, Uppsalaan 12, 3584 CT Utrecht, The Netherlands
UMR 518 MIA-Paris, INRAE, Université Paris-Saclay, 75013 Paris, France
Author to whom correspondence should be addressed.
Cancers 2020, 12(7), 1785;
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
Show Figures

Figure 1

MDPI and ACS Style

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop