A Comparative Study of Feature Selection and Classification Techniques for High-Throughput DNA Methylation Data

Alkuhlani, Alhasan; Nassef, Mohammad; Farag, Ibrahim

doi:10.1007/978-3-319-48308-5_76

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…Choosing the best filter approach depends very much on the level of computational resources available to the researcher [22]. Wrapper methods in contrast utilize performance metrics of the predictive model to select the best feature subsets while embedded methods include feature-selection in the process of the modelling algorithm's execution [23,24]. These feature-selection methods naturally lead to dimension reduction but there are other methods which can also achieve this.…”

Section: Introductionmentioning

confidence: 99%

A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator

Doherty

Dempster

Hannon

et al. 2022

Preprint

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The field of epigenomics holds great promise in understanding and treating disease with advances in machine learning (ML) and artificial intelligence being vitally important in this pursuit. Increasingly, research now utilises DNA methylation measures at cytosine-guanine dinucleotides (CpG) to detect disease and estimate biological traits such as aging. Given the high dimensionality of DNA methylation data, feature-selection techniques are commonly employed to reduce dimensionality and identify the most important subset of features. In this study, we test and compare a range of feature-selection methods and ML algorithms in the development of a novel DNA methylation-based telomere length (TL) estimator. We found that principal component analysis in advance of elastic net regression led to the overall best performing estimator when evaluated using a nested cross-validation analysis and two independent test cohorts. In contrast, the baseline model of elastic net regression with no prior feature reduction stage performed worst - suggesting a prior feature-selection stage may have important utility. The variance in performance across tested approaches shows that estimators are sensitive to data set heterogeneity and the development of an optimal DNA methylation-based estimator should benefit from the robust methodological approach used in this study. Additionally, we observed that different DNA methylation-based TL estimators, which have few common CpGs, are associated with many of the same biological entities. Moreover, our methodology which utilises a range of feature-selection approaches and ML algorithms could be applied to other biological markers and disease phenotypes, to examine their relationship with DNA methylation and predictive value.

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Section: Introductionmentioning

confidence: 99%

A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator

Doherty

Dempster

Hannon

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator

et al. 2023

View full text Add to dashboard Cite

Background The field of epigenomics holds great promise in understanding and treating disease with advances in machine learning (ML) and artificial intelligence being vitally important in this pursuit. Increasingly, research now utilises DNA methylation measures at cytosine–guanine dinucleotides (CpG) to detect disease and estimate biological traits such as aging. Given the challenge of high dimensionality of DNA methylation data, feature-selection techniques are commonly employed to reduce dimensionality and identify the most important subset of features. In this study, our aim was to test and compare a range of feature-selection methods and ML algorithms in the development of a novel DNA methylation-based telomere length (TL) estimator. We utilised both nested cross-validation and two independent test sets for the comparisons. Results We found that principal component analysis in advance of elastic net regression led to the overall best performing estimator when evaluated using a nested cross-validation analysis and two independent test cohorts. This approach achieved a correlation between estimated and actual TL of 0.295 (83.4% CI [0.201, 0.384]) on the EXTEND test data set. Contrastingly, the baseline model of elastic net regression with no prior feature reduction stage performed less well in general—suggesting a prior feature-selection stage may have important utility. A previously developed TL estimator, DNAmTL, achieved a correlation of 0.216 (83.4% CI [0.118, 0.310]) on the EXTEND data. Additionally, we observed that different DNA methylation-based TL estimators, which have few common CpGs, are associated with many of the same biological entities. Conclusions The variance in performance across tested approaches shows that estimators are sensitive to data set heterogeneity and the development of an optimal DNA methylation-based estimator should benefit from the robust methodological approach used in this study. Moreover, our methodology which utilises a range of feature-selection approaches and ML algorithms could be applied to other biological markers and disease phenotypes, to examine their relationship with DNA methylation and predictive value.

show abstract

A Comparative Study of Feature Selection and Classification Techniques for High-Throughput DNA Methylation Data

Cited by 2 publications

References 25 publications

A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator

A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator

A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator

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