Interpretable machine learning algorithms reveal gut microbiome features associated with atopic dermatitis
BackgroundThe “gut–skin axis” has been proposed to play an important role in the development and symptoms of atopic dermatitis. Therefore, we have constructed an interpretable machine learning framework to quantitatively screen key gut flora.MethodsThe 16S rRNA dataset, after applying the centered l...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1528046/full |
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| author | Jingtai Ma Jingtai Ma Yiting Fang Yiting Fang Shiqi Li Shiqi Li Lilian Zeng Siyi Chen Zhifeng Li Guiyuan Ji Xingfen Yang Wei Wu Wei Wu |
| author_facet | Jingtai Ma Jingtai Ma Yiting Fang Yiting Fang Shiqi Li Shiqi Li Lilian Zeng Siyi Chen Zhifeng Li Guiyuan Ji Xingfen Yang Wei Wu Wei Wu |
| author_sort | Jingtai Ma |
| collection | DOAJ |
| description | BackgroundThe “gut–skin axis” has been proposed to play an important role in the development and symptoms of atopic dermatitis. Therefore, we have constructed an interpretable machine learning framework to quantitatively screen key gut flora.MethodsThe 16S rRNA dataset, after applying the centered log-ratio transformation, was analyzed using five different machine learning models: random forest, light gradient boosting machine, extreme gradient boosting, support vector machine with radial kernel, and logistic regression. Interpretable machine learning methods, such as SHAP values, were used to identify significant features associated with atopic dermatitis.ResultsRandom forest performed better than the other “tree” models in the validation partitions. The SHAP global dependency plot indicated that Bifidobacterium ranked as the strongest predictive factor across all prediction horizons, although the SHAP values for some features were still higher in support vector machine and logistic regression models. The SHAP partial dependency plot for “tree” models showed that the best segmentation point for Bifidobacterium was further from the origin compared to other features in the respective models, quantitatively reflecting differences in gut microbiota.ConclusionMachine learning models combined with SHAP could be used to quantitatively screen key gut flora in atopic dermatitis patients, providing doctors with an intuitive understanding of 16S rRNA sequencing data to support precision medicine in care and recovery. |
| format | Article |
| id | doaj-art-ab27d4d5fa044e26a0c63bc09f87359c |
| institution | OA Journals |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-ab27d4d5fa044e26a0c63bc09f87359c2025-08-20T01:47:58ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-05-011610.3389/fimmu.2025.15280461528046Interpretable machine learning algorithms reveal gut microbiome features associated with atopic dermatitisJingtai Ma0Jingtai Ma1Yiting Fang2Yiting Fang3Shiqi Li4Shiqi Li5Lilian Zeng6Siyi Chen7Zhifeng Li8Guiyuan Ji9Xingfen Yang10Wei Wu11Wei Wu12National Medical Products Administration (NMPA) Key Laboratory for Safety Evaluation of Cosmetics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, ChinaGuangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, ChinaNational Medical Products Administration (NMPA) Key Laboratory for Safety Evaluation of Cosmetics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, ChinaGuangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, ChinaNational Medical Products Administration (NMPA) Key Laboratory for Safety Evaluation of Cosmetics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, ChinaGuangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, ChinaGuangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, ChinaGuangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, ChinaGuangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, ChinaGuangdong Provincial Institute of Public Health, Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, ChinaNational Medical Products Administration (NMPA) Key Laboratory for Safety Evaluation of Cosmetics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, ChinaNational Medical Products Administration (NMPA) Key Laboratory for Safety Evaluation of Cosmetics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, ChinaGuangdong Provincial Center for Disease Control and Prevention, Guangzhou, ChinaBackgroundThe “gut–skin axis” has been proposed to play an important role in the development and symptoms of atopic dermatitis. Therefore, we have constructed an interpretable machine learning framework to quantitatively screen key gut flora.MethodsThe 16S rRNA dataset, after applying the centered log-ratio transformation, was analyzed using five different machine learning models: random forest, light gradient boosting machine, extreme gradient boosting, support vector machine with radial kernel, and logistic regression. Interpretable machine learning methods, such as SHAP values, were used to identify significant features associated with atopic dermatitis.ResultsRandom forest performed better than the other “tree” models in the validation partitions. The SHAP global dependency plot indicated that Bifidobacterium ranked as the strongest predictive factor across all prediction horizons, although the SHAP values for some features were still higher in support vector machine and logistic regression models. The SHAP partial dependency plot for “tree” models showed that the best segmentation point for Bifidobacterium was further from the origin compared to other features in the respective models, quantitatively reflecting differences in gut microbiota.ConclusionMachine learning models combined with SHAP could be used to quantitatively screen key gut flora in atopic dermatitis patients, providing doctors with an intuitive understanding of 16S rRNA sequencing data to support precision medicine in care and recovery.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1528046/fullmachine learningrandom forestlight gradient boosting machineextreme gradient boostingSHAP valuepartial dependence plot |
| spellingShingle | Jingtai Ma Jingtai Ma Yiting Fang Yiting Fang Shiqi Li Shiqi Li Lilian Zeng Siyi Chen Zhifeng Li Guiyuan Ji Xingfen Yang Wei Wu Wei Wu Interpretable machine learning algorithms reveal gut microbiome features associated with atopic dermatitis Frontiers in Immunology machine learning random forest light gradient boosting machine extreme gradient boosting SHAP value partial dependence plot |
| title | Interpretable machine learning algorithms reveal gut microbiome features associated with atopic dermatitis |
| title_full | Interpretable machine learning algorithms reveal gut microbiome features associated with atopic dermatitis |
| title_fullStr | Interpretable machine learning algorithms reveal gut microbiome features associated with atopic dermatitis |
| title_full_unstemmed | Interpretable machine learning algorithms reveal gut microbiome features associated with atopic dermatitis |
| title_short | Interpretable machine learning algorithms reveal gut microbiome features associated with atopic dermatitis |
| title_sort | interpretable machine learning algorithms reveal gut microbiome features associated with atopic dermatitis |
| topic | machine learning random forest light gradient boosting machine extreme gradient boosting SHAP value partial dependence plot |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1528046/full |
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