Modelling Asthma Patients’ Responsiveness to Treatment Using Feature Selection and Evolutionary Computation

Lopez-Rincon, Alejandro; Roozendaal, Daphne S.; Spierenburg, Hilde M.; Holm, Asta L.; Metcalf, Renee; Perez‐Pardo, Paula; Kraneveld, Aletta D.; Tonda, Alberto

doi:10.1007/978-3-030-72699-7_23

Applications of Evolutionary Computation

2021

DOI: 10.1007/978-3-030-72699-7_23

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Modelling Asthma Patients’ Responsiveness to Treatment Using Feature Selection and Evolutionary Computation

Alejandro Lopez-Rincon¹,

Daphne S. Roozendaal²,

Hilde M. Spierenburg³

et al.

Abstract: HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de

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2024

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References 34 publications

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A robust microbiome signature for autism spectrum disorder across different studies using machine learning

Peralta-Marzal,

Rojas-Velazquez,

Rigters

et al. 2024

Sci Rep

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Autism spectrum disorder (ASD) is a highly complex neurodevelopmental disorder characterized by deficits in sociability and repetitive behaviour, however there is a great heterogeneity within other comorbidities that accompany ASD. Recently, gut microbiome has been pointed out as a plausible contributing factor for ASD development as individuals diagnosed with ASD often suffer from intestinal problems and show a differentiated intestinal microbial composition. Nevertheless, gut microbiome studies in ASD rarely agree on the specific bacterial taxa involved in this disorder. Regarding the potential role of gut microbiome in ASD pathophysiology, our aim is to investigate whether there is a set of bacterial taxa relevant for ASD classification by using a sibling-controlled dataset. Additionally, we aim to validate these results across two independent cohorts as several confounding factors, such as lifestyle, influence both ASD and gut microbiome studies. A machine learning approach, recursive ensemble feature selection (REFS), was applied to 16S rRNA gene sequencing data from 117 subjects (60 ASD cases and 57 siblings) identifying 26 bacterial taxa that discriminate ASD cases from controls. The average area under the curve (AUC) of this specific set of bacteria in the sibling-controlled dataset was 81.6%. Moreover, we applied the selected bacterial taxa in a tenfold cross-validation scheme using two independent cohorts (a total of 223 samples—125 ASD cases and 98 controls). We obtained average AUCs of 74.8% and 74%, respectively. Analysis of the gut microbiome using REFS identified a set of bacterial taxa that can be used to predict the ASD status of children in three distinct cohorts with AUC over 80% for the best-performing classifiers. Our results indicate that the gut microbiome has a strong association with ASD and should not be disregarded as a potential target for therapeutic interventions. Furthermore, our work can contribute to use the proposed approach for identifying microbiome signatures across other 16S rRNA gene sequencing datasets.

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A robust microbiome signature for autism spectrum disorder across different studies using machine learning

Peralta-Marzal,

Rojas-Velazquez,

Rigters

et al. 2024

Sci Rep

Self Cite

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show abstract

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Modelling Asthma Patients’ Responsiveness to Treatment Using Feature Selection and Evolutionary Computation

Cited by 1 publication

References 34 publications

A robust microbiome signature for autism spectrum disorder across different studies using machine learning

A robust microbiome signature for autism spectrum disorder across different studies using machine learning

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