A polygenic stacking classifier revealed the complicated platelet transcriptomic landscape of adult immune thrombocytopenia

Immune thrombocytopenia (ITP) is an autoimmune disease with the typical symptom of a low platelet count in blood. ITP demonstrated age and sex biases in both occurrences and prognosis, and adult ITP was mainly induced by the living environments. The current diagnosis guideline lacks the integration...

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Autores principales: Xu, Chengfeng, Zhang, Ruochi, Duan, Meiyu, Zhou, Yongming, Bao, Jizhang, Lu, Hao, Wang, Jie, Hu, Minghui, Hu, Zhaoyang, Zhou, Fengfeng, Zhu, Wenwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society of Gene & Cell Therapy 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9046129/
https://www.ncbi.nlm.nih.gov/pubmed/35505964
http://dx.doi.org/10.1016/j.omtn.2022.04.004
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author Xu, Chengfeng
Zhang, Ruochi
Duan, Meiyu
Zhou, Yongming
Bao, Jizhang
Lu, Hao
Wang, Jie
Hu, Minghui
Hu, Zhaoyang
Zhou, Fengfeng
Zhu, Wenwei
author_facet Xu, Chengfeng
Zhang, Ruochi
Duan, Meiyu
Zhou, Yongming
Bao, Jizhang
Lu, Hao
Wang, Jie
Hu, Minghui
Hu, Zhaoyang
Zhou, Fengfeng
Zhu, Wenwei
author_sort Xu, Chengfeng
collection PubMed
description Immune thrombocytopenia (ITP) is an autoimmune disease with the typical symptom of a low platelet count in blood. ITP demonstrated age and sex biases in both occurrences and prognosis, and adult ITP was mainly induced by the living environments. The current diagnosis guideline lacks the integration of molecular heterogenicity. This study recruited the largest cohort of platelet transcriptome samples. A comprehensive procedure of feature selection, feature engineering, and stacking classification was carried out to detect the ITP biomarkers using RNA sequencing (RNA-seq) transcriptomes. The 40 detected biomarkers were loaded to train the final ITP detection model, with an overall accuracy 0.974. The biomarkers suggested that ITP onset may be associated with various transcribed components, including protein-coding genes, long intergenic non-coding RNA (lincRNA) genes, and pseudogenes with apparent transcriptions. The delivered ITP detection model may also be utilized as a complementary ITP diagnosis tool. The code and the example dataset is freely available on http://www.healthinformaticslab.org/supp/resources.php
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spelling pubmed-90461292022-05-02 A polygenic stacking classifier revealed the complicated platelet transcriptomic landscape of adult immune thrombocytopenia Xu, Chengfeng Zhang, Ruochi Duan, Meiyu Zhou, Yongming Bao, Jizhang Lu, Hao Wang, Jie Hu, Minghui Hu, Zhaoyang Zhou, Fengfeng Zhu, Wenwei Mol Ther Nucleic Acids Original Article Immune thrombocytopenia (ITP) is an autoimmune disease with the typical symptom of a low platelet count in blood. ITP demonstrated age and sex biases in both occurrences and prognosis, and adult ITP was mainly induced by the living environments. The current diagnosis guideline lacks the integration of molecular heterogenicity. This study recruited the largest cohort of platelet transcriptome samples. A comprehensive procedure of feature selection, feature engineering, and stacking classification was carried out to detect the ITP biomarkers using RNA sequencing (RNA-seq) transcriptomes. The 40 detected biomarkers were loaded to train the final ITP detection model, with an overall accuracy 0.974. The biomarkers suggested that ITP onset may be associated with various transcribed components, including protein-coding genes, long intergenic non-coding RNA (lincRNA) genes, and pseudogenes with apparent transcriptions. The delivered ITP detection model may also be utilized as a complementary ITP diagnosis tool. The code and the example dataset is freely available on http://www.healthinformaticslab.org/supp/resources.php American Society of Gene & Cell Therapy 2022-04-06 /pmc/articles/PMC9046129/ /pubmed/35505964 http://dx.doi.org/10.1016/j.omtn.2022.04.004 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Original Article
Xu, Chengfeng
Zhang, Ruochi
Duan, Meiyu
Zhou, Yongming
Bao, Jizhang
Lu, Hao
Wang, Jie
Hu, Minghui
Hu, Zhaoyang
Zhou, Fengfeng
Zhu, Wenwei
A polygenic stacking classifier revealed the complicated platelet transcriptomic landscape of adult immune thrombocytopenia
title A polygenic stacking classifier revealed the complicated platelet transcriptomic landscape of adult immune thrombocytopenia
title_full A polygenic stacking classifier revealed the complicated platelet transcriptomic landscape of adult immune thrombocytopenia
title_fullStr A polygenic stacking classifier revealed the complicated platelet transcriptomic landscape of adult immune thrombocytopenia
title_full_unstemmed A polygenic stacking classifier revealed the complicated platelet transcriptomic landscape of adult immune thrombocytopenia
title_short A polygenic stacking classifier revealed the complicated platelet transcriptomic landscape of adult immune thrombocytopenia
title_sort polygenic stacking classifier revealed the complicated platelet transcriptomic landscape of adult immune thrombocytopenia
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9046129/
https://www.ncbi.nlm.nih.gov/pubmed/35505964
http://dx.doi.org/10.1016/j.omtn.2022.04.004
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