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\begin{document}

\title{A robust mRNA signature obtained via Recursive Ensemble Feature
Selection predicts the responsiveness of omalizumab in
moderate-to-severe asthma}



\author[1]{Alejandro Lopez-Rincon}%
\author[1]{S. Kidwai}%
\author[2]{P. Barbiero}%
\author[1]{I. Meijerman}%
\author[3]{A. Tonda}%
\author[1]{* PPerez-Pardo}%
\author[2]{P. Lio}%
\author[4]{Anke-Hilse Maitland-van der Zee}%
\author[5]{D. L. Oberski}%
\author[1]{Aletta Kraneveld}%
\affil[1]{Universiteit Utrecht Utrechts Instituut voor Farmaceutische Wetenschappen}%
\affil[2]{University of Cambridge Computer Laboratory}%
\affil[3]{Fondation de cooperation scientifique Campus Paris-Saclay}%
\affil[4]{Amsterdam Universitair Medische Centra}%
\affil[5]{Universitair Medisch Centrum Utrecht}%


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  \date{May 26, 2024}


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\begin{abstract}
\textbf{Background}: Not being well controlled by therapy with inhaled
corticosteroids and long-acting \selectlanguage{greek}β\selectlanguage{english}2 agonist bronchodilators is a major
concern for severe-asthma patients. Current treatment option for these
patients is the use of biologicals such as anti-IgE treatment,
omalizumab, as add-on therapy. Despite the accepted use of omalizumab,
patients do not always benefit from it. Therefore, there is a need to
identify reliable biomarkers as predictors of omalizumab response.
\textbf{Methods}: Two novel computational algorithms, machine-learning
based Recursive Ensemble Feature Selection (REFS) and rule-based
algorithm Logic Explainable Networks (LEN ) were used on open accessible
mRNA expression data from moderate-to-severe asthma patients to identify
genes as predictors of omalizumab response \textbf{Results}: With REFS,
the number of features were reduced from 28,402 genes to 5 genes while
obtaining a cross-validated accuracy of 0.975. The 5 responsiveness
predictive genes encode for the following proteins: Coiled-coil domain-
containing protein 113 (CCDC113), Solute Carrier Family 26 Member 8
(SLC26A), Protein Phosphatase 1 Regulatory Subunit 3D (PPP1R3D), C-Type
lectin Domain Family 4 member C (CLEC4C) and LOC100131780 (not
annotated). The LEN algorithm found 4 identical genes with REFS: CCDC113
,SLC26A8 PPP1R3D and LOC100131780. Literature research showed that the 4
identified responsiveness predicting genes are associated with: mucosal
immunity, cell metabolism, and airway remodeling. \textbf{Conclusion and
clinical relevance}: Both computational methods show 4 identical genes
as predictors of omalizumab response in moderate-to-severe asthma
patients. The obtained high accuracy indicates that our approach has
potential for clinical settings. Future studies in relevant cohort data
should validate our computational approach.%
\end{abstract}%



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\textbf{Hosted file}

\verb`Main text_Kidwai et al., A robust mRNA signature obtained via Recursive Ensemble Feature Selection predicts the responsiveness of omalizumab in.docx`  available at \url{https://authorea.com/users/517143/articles/591786-a-robust-mrna-signature-obtained-via-recursive-ensemble-feature-selection-predicts-the-responsiveness-of-omalizumab-in-moderate-to-severe-asthma}

\textbf{Hosted file}

\verb`Figures_Kidwai et al FINAL .docx`  available at \url{https://authorea.com/users/517143/articles/591786-a-robust-mrna-signature-obtained-via-recursive-ensemble-feature-selection-predicts-the-responsiveness-of-omalizumab-in-moderate-to-severe-asthma}

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