Using a Machine Learning Approach to Identify Low-Frequency and Rare FLG Alleles Associated with Remission of Atopic Dermatitis

Berna, Ronald; Mitra, Nandita; Hoffstad, Ole; Wubbenhorst, Bradley; Nathanson, Katherine L.; Margolis, David J.

doi:10.1016/j.xjidi.2021.100046

Cited by 4 publications

(2 citation statements)

References 50 publications

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“…Targeted sequencing approaches have also been employed to facilitate the identification of rare, functional variants in the genetic background of AD. Despite the contribution of loss-of-function FLG SNPs in the AD risk, machine-learning approaches revealed 48 low-frequency variants associated with increased self-reported remission after 6 months in 326 African American patients from the Pediatric Eczema Elective Registry (PEER) cohort in the absence of medications, 16 of which preserving the significant signals in an independent cohort [ 42 ]. The PEER cohort was utilized in additional studies reporting significant associations with rare variants mapped to FLG2 and TCHHL1 genes in both African American ( n = 326) and white ( n = 379) AD patients [ 43 ].…”

Section: Genetics Of Atopic Dermatitismentioning

confidence: 99%

Intrinsic Effects of Exposome in Atopic Dermatitis: Genomics, Epigenomics and Regulatory Layers

Grafanaki

Antonatos

Maniatis

et al. 2023

JCM

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Atopic dermatitis (AD) or atopic eczema is an increasingly manifested inflammatory skin disorder of complex etiology which is modulated by both extrinsic and intrinsic factors. The exposome includes a person’s lifetime exposures and their effects. We recently reviewed the extrinsic exposome’s environmental risk factors that contribute to AD. The periods of pregnancy, infancy, and teenage years are recognized as crucial stages in the formation of AD, where the exposome leads to enduring impacts on the immune system. However, research is now focusing on the interactions between intrinsic pathways that are modulated by the extrinsic exposome, including genetic variation, epigenetic modifications, and signals, such as diet, stress, and microbiome interactions. As a result, immune dysregulation, barrier dysfunction, hormonal fluctuations, and skin microbiome dysbiosis are important factors contributing to AD development, and their in-depth understanding is crucial not only for AD treatment but also for similar inflammatory disorders.

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Section: Genetics Of Atopic Dermatitismentioning

confidence: 99%

Intrinsic Effects of Exposome in Atopic Dermatitis: Genomics, Epigenomics and Regulatory Layers

Grafanaki

Antonatos

Maniatis

et al. 2023

JCM

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“…They performed cross-validation at different skin inflammation conditions and disease stages by using co-expression clustering and machine learning tools, ultimately revealing the impact of keratin-forming cell programming on skin inflammation and suggesting that perturbation of uniaxial immune signaling alone may not be sufficient to resolve keratin-forming cell immunophenotype abnormalities. Berna et al (2021) constructed a machine learning framework for exploring the association between AD pathogenesis and low-frequency, rare alleles. However, because of the variety of factors that influence the physiological status of AD.…”

Section: Introductionmentioning

confidence: 99%

bSRWPSO-FKNN: A boosted PSO with fuzzy K-nearest neighbor classifier for predicting atopic dermatitis disease

Zhao²,

Xu³

et al. 2023

Front. Neuroinform.

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IntroductionAtopic dermatitis (AD) is an allergic disease with extreme itching that bothers patients. However, diagnosing AD depends on clinicians’ subjective judgment, which may be missed or misdiagnosed sometimes.MethodsThis paper establishes a medical prediction model for the first time on the basis of the enhanced particle swarm optimization (SRWPSO) algorithm and the fuzzy K-nearest neighbor (FKNN), called bSRWPSO-FKNN, which is practiced on a dataset related to patients with AD. In SRWPSO, the Sobol sequence is introduced into particle swarm optimization (PSO) to make the particle distribution of the initial population more uniform, thus improving the population’s diversity and traversal. At the same time, this study also adds a random replacement strategy and adaptive weight strategy to the population updating process of PSO to overcome the shortcomings of poor convergence accuracy and easily fall into the local optimum of PSO. In bSRWPSO-FKNN, the core of which is to optimize the classification performance of FKNN through binary SRWPSO.ResultsTo prove that the study has scientific significance, this paper first successfully demonstrates the core advantages of SRWPSO in well-known algorithms through benchmark function validation experiments. Secondly, this article demonstrates that the bSRWPSO-FKNN has practical medical significance and effectiveness through nine public and medical datasets.DiscussionThe 10 times 10-fold cross-validation experiments demonstrate that bSRWPSO-FKNN can pick up the key features of AD, including the content of lymphocytes (LY), Cat dander, Milk, Dermatophagoides Pteronyssinus/Farinae, Ragweed, Cod, and Total IgE. Therefore, the established bSRWPSO-FKNN method practically aids in the diagnosis of AD.

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Uncommon variants in FLG2 and TCHHL1 are associated with remission of atopic dermatitis in a large longitudinal US cohort

et al. 2022

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Using a Machine Learning Approach to Identify Low-Frequency and Rare FLG Alleles Associated with Remission of Atopic Dermatitis

Abstract: Using a machine learning approach to identify low-frequency and rare filaggrin alleles associated with remission of atopic dermatitis,

Cited by 4 publications

References 50 publications

Intrinsic Effects of Exposome in Atopic Dermatitis: Genomics, Epigenomics and Regulatory Layers

Intrinsic Effects of Exposome in Atopic Dermatitis: Genomics, Epigenomics and Regulatory Layers

bSRWPSO-FKNN: A boosted PSO with fuzzy K-nearest neighbor classifier for predicting atopic dermatitis disease

Uncommon variants in FLG2 and TCHHL1 are associated with remission of atopic dermatitis in a large longitudinal US cohort

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