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  1. Article ; Online: Deep learning for detecting and elucidating human T-cell leukemia virus type 1 integration in the human genome.

    Xu, Haodong / Jia, Johnathan / Jeong, Hyun-Hwan / Zhao, Zhongming

    Patterns (New York, N.Y.)

    2023  Volume 4, Issue 2, Page(s) 100674

    Abstract: Human T-cell leukemia virus type 1 (HTLV-1), a retrovirus, is the causative agent for adult T cell leukemia/lymphoma and many other human diseases. Accurate and high throughput detection of HTLV-1 virus integration sites (VISs) across the host genomes ... ...

    Abstract Human T-cell leukemia virus type 1 (HTLV-1), a retrovirus, is the causative agent for adult T cell leukemia/lymphoma and many other human diseases. Accurate and high throughput detection of HTLV-1 virus integration sites (VISs) across the host genomes plays a crucial role in the prevention and treatment of HTLV-1-associated diseases. Here, we developed DeepHTLV, the first deep learning framework for VIS prediction
    Language English
    Publishing date 2023-02-10
    Publishing country United States
    Document type Journal Article
    ISSN 2666-3899
    ISSN (online) 2666-3899
    DOI 10.1016/j.patter.2022.100674
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Delineating COVID-19 immunological features using single-cell RNA sequencing.

    Liu, Wendao / Jia, Johnathan / Dai, Yulin / Chen, Wenhao / Pei, Guangsheng / Yan, Qiheng / Zhao, Zhongming

    Innovation (Cambridge (Mass.))

    2022  Volume 3, Issue 5, Page(s) 100289

    Abstract: Understanding the molecular mechanisms of coronavirus disease 2019 (COVID-19) pathogenesis and immune response is vital for developing therapies. Single-cell RNA sequencing has been applied to delineate the cellular heterogeneity of the host response ... ...

    Abstract Understanding the molecular mechanisms of coronavirus disease 2019 (COVID-19) pathogenesis and immune response is vital for developing therapies. Single-cell RNA sequencing has been applied to delineate the cellular heterogeneity of the host response toward COVID-19 in multiple tissues and organs. Here, we review the applications and findings from over 80 original COVID-19 single-cell RNA sequencing studies as well as many secondary analysis studies. We describe that single-cell RNA sequencing reveals multiple features of COVID-19 patients with different severity, including cell populations with proportional alteration, COVID-19-induced genes and pathways, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in single cells, and adaptation of immune repertoire. We also collect published single-cell RNA sequencing datasets from original studies. Finally, we discuss the limitations in current studies and perspectives for future advance.
    Language English
    Publishing date 2022-07-21
    Publishing country United States
    Document type Journal Article ; Review
    ISSN 2666-6758
    ISSN (online) 2666-6758
    DOI 10.1016/j.xinn.2022.100289
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Investigating Cellular Trajectories in the Severity of COVID-19 and Their Transcriptional Programs Using Machine Learning Approaches

    Jeong, Hyun-Hwan / Jia, Johnathan / Dai, Yulin / Simon, Lukas M / Zhao, Zhongming

    Genes. 2021 Apr. 24, v. 12, no. 5

    2021  

    Abstract: Single-cell RNA sequencing of the bronchoalveolar lavage fluid (BALF) samples from COVID-19 patients has enabled us to examine gene expression changes of human tissue in response to the SARS-CoV-2 virus infection. However, the underlying mechanisms of ... ...

    Abstract Single-cell RNA sequencing of the bronchoalveolar lavage fluid (BALF) samples from COVID-19 patients has enabled us to examine gene expression changes of human tissue in response to the SARS-CoV-2 virus infection. However, the underlying mechanisms of COVID-19 pathogenesis at single-cell resolution, its transcriptional drivers, and dynamics require further investigation. In this study, we applied machine learning algorithms to infer the trajectories of cellular changes and identify their transcriptional programs. Our study generated cellular trajectories that show the COVID-19 pathogenesis of healthy-to-moderate and healthy-to-severe on macrophages and T cells, and we observed more diverse trajectories in macrophages compared to T cells. Furthermore, our deep-learning algorithm DrivAER identified several pathways (e.g., xenobiotic pathway and complement pathway) and transcription factors (e.g., MITF and GATA3) that could be potential drivers of the transcriptomic changes for COVID-19 pathogenesis and the markers of the COVID-19 severity. Moreover, macrophages-related functions corresponded more to the disease severity compared to T cells-related functions. Our findings more proficiently dissected the transcriptomic changes leading to the severity of a COVID-19 infection.
    Keywords COVID-19 infection ; GATA transcription factors ; RNA ; Severe acute respiratory syndrome coronavirus 2 ; algorithms ; complement ; disease severity ; gene expression ; humans ; macrophages ; pathogenesis ; transcription (genetics) ; transcriptomics ; viruses ; xenobiotics
    Language English
    Dates of publication 2021-0424
    Publishing place Multidisciplinary Digital Publishing Institute
    Document type Article
    Note NAL-AP-2-clean
    ZDB-ID 2527218-4
    ISSN 2073-4425
    ISSN 2073-4425
    DOI 10.3390/genes12050635
    Database NAL-Catalogue (AGRICOLA)

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  4. Article ; Online: Investigating Cellular Trajectories in the Severity of COVID-19 and Their Transcriptional Programs Using Machine Learning Approaches.

    Jeong, Hyun-Hwan / Jia, Johnathan / Dai, Yulin / Simon, Lukas M / Zhao, Zhongming

    Genes

    2021  Volume 12, Issue 5

    Abstract: Single-cell RNA sequencing of the bronchoalveolar lavage fluid (BALF) samples from COVID-19 patients has enabled us to examine gene expression changes of human tissue in response to the SARS-CoV-2 virus infection. However, the underlying mechanisms of ... ...

    Abstract Single-cell RNA sequencing of the bronchoalveolar lavage fluid (BALF) samples from COVID-19 patients has enabled us to examine gene expression changes of human tissue in response to the SARS-CoV-2 virus infection. However, the underlying mechanisms of COVID-19 pathogenesis at single-cell resolution, its transcriptional drivers, and dynamics require further investigation. In this study, we applied machine learning algorithms to infer the trajectories of cellular changes and identify their transcriptional programs. Our study generated cellular trajectories that show the COVID-19 pathogenesis of healthy-to-moderate and healthy-to-severe on macrophages and T cells, and we observed more diverse trajectories in macrophages compared to T cells. Furthermore, our deep-learning algorithm DrivAER identified several pathways (e.g., xenobiotic pathway and complement pathway) and transcription factors (e.g., MITF and GATA3) that could be potential drivers of the transcriptomic changes for COVID-19 pathogenesis and the markers of the COVID-19 severity. Moreover, macrophages-related functions corresponded more to the disease severity compared to T cells-related functions. Our findings more proficiently dissected the transcriptomic changes leading to the severity of a COVID-19 infection.
    MeSH term(s) Algorithms ; Bronchoalveolar Lavage Fluid/virology ; COVID-19/etiology ; COVID-19/genetics ; COVID-19/pathology ; Computational Biology/methods ; Gene Expression Profiling ; Humans ; Machine Learning ; Macrophages/physiology ; Macrophages/virology ; Sequence Analysis, RNA/methods ; Single-Cell Analysis ; T-Lymphocytes/physiology ; T-Lymphocytes/virology
    Language English
    Publishing date 2021-04-24
    Publishing country Switzerland
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2527218-4
    ISSN 2073-4425 ; 2073-4425
    ISSN (online) 2073-4425
    ISSN 2073-4425
    DOI 10.3390/genes12050635
    Database MEDical Literature Analysis and Retrieval System OnLINE

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