Identification of Cell Markers and Their Expression Patterns in Skin Based on Single-Cell RNA-Sequencing Profiles

Zhou, Xianchao; Ding, Shi‐Jian; Wang, Deling; Lei, Chen; Kong, Feng; Huang, Tao; Li, Zhandong; Cai, Yu-Dong

doi:10.3390/life12040550

Cited by 24 publications

(20 citation statements)

References 83 publications

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“…Random forest is widely applied in analyzing biological and biomedical data. Several previous studies indicate the satisfactory performance of RF ( Pan et al, 2010 ; Zhao et al, 2018 ; Jia et al, 2020 ; Chen et al, 2021 , 2022 ; Ding et al, 2022 ; Li Z. et al, 2022 ; Wu and Chen, 2022 ; Zhou et al, 2022 ). RF is a meta-classifier because it consists of numerous decision trees.…”

Section: Methodsmentioning

confidence: 93%

Detecting Brain Structure-Specific Methylation Signatures and Rules for Alzheimer’s Disease

Guo

Zeng

et al. 2022

Front. Neurosci.

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Alzheimer’s disease (AD) is a progressive disease that leads to irreversible behavioral changes, erratic emotions, and loss of motor skills. These conditions make people with AD hard or almost impossible to take care of. Multiple internal and external pathological factors may affect or even trigger the initiation and progression of AD. DNA methylation is one of the most effective regulatory roles during AD pathogenesis, and pathological methylation alterations may be potentially different in the various brain structures of people with AD. Although multiple loci associated with AD initiation and progression have been identified, the spatial distribution patterns of AD-associated DNA methylation in the brain have not been clarified. According to the systematic methylation profiles on different structural brain regions, we applied multiple machine learning algorithms to investigate such profiles. First, the profile on each brain region was analyzed by the Boruta feature filtering method. Some important methylation features were extracted and further analyzed by the max-relevance and min-redundancy method, resulting in a feature list. Then, the incremental feature selection method, incorporating some classification algorithms, adopted such list to identify candidate AD-associated loci at methylation with structural specificity, establish a group of quantitative rules for revealing the effects of DNA methylation in various brain regions (i.e., four brain structures) on AD pathogenesis. Furthermore, some efficient classifiers based on essential methylation sites were proposed to identify AD samples. Results revealed that methylation alterations in different brain structures have different contributions to AD pathogenesis. This study further illustrates the complex pathological mechanisms of AD.

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Section: Methodsmentioning

confidence: 93%

Detecting Brain Structure-Specific Methylation Signatures and Rules for Alzheimer’s Disease

Guo

Zeng

et al. 2022

Front. Neurosci.

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“…In this study, we tried four algorithms: DT [ 36 ], k-nearest neighbor (KNN) [ 37 ], RF [ 38 ], and support vector machine (SVM) [ 39 ]. They have wide applications in dealing with biological and medical problems [ 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ].…”

Section: Methodsmentioning

confidence: 99%

Identifying COVID-19 Severity-Related SARS-CoV-2 Mutation Using a Machine Learning Method

Huang

Chen

Guo

et al. 2022

Life

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SARS-CoV-2 shows great evolutionary capacity through a high frequency of genomic variation during transmission. Evolved SARS-CoV-2 often demonstrates resistance to previous vaccines and can cause poor clinical status in patients. Mutations in the SARS-CoV-2 genome involve mutations in structural and nonstructural proteins, and some of these proteins such as spike proteins have been shown to be directly associated with the clinical status of patients with severe COVID-19 pneumonia. In this study, we collected genome-wide mutation information of virulent strains and the severity of COVID-19 pneumonia in patients varying depending on their clinical status. Important protein mutations and untranslated region mutations were extracted using machine learning methods. First, through Boruta and four ranking algorithms (least absolute shrinkage and selection operator, light gradient boosting machine, max-relevance and min-redundancy, and Monte Carlo feature selection), mutations that were highly correlated with the clinical status of the patients were screened out and sorted in four feature lists. Some mutations such as D614G and V1176F were shown to be associated with viral infectivity. Moreover, previously unreported mutations such as A320V of nsp14 and I164ILV of nsp14 were also identified, which suggests their potential roles. We then applied the incremental feature selection method to each feature list to construct efficient classifiers, which can be directly used to distinguish the clinical status of COVID-19 patients. Meanwhile, four sets of quantitative rules were set up, which can help us to more intuitively understand the role of each mutation in differentiating the clinical status of COVID-19 patients. Identified key mutations linked to virologic properties will help better understand the mechanisms of infection and will aid in the development of antiviral treatments.

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“…The method compares the value of the original features to the significance achievable at random, as indicated by their permuted copies, and gradually removes unnecessary features to stabilize the test. In the last few years, Boruta has been widely used in processing biological data ( Chen et al, 2021a ; Huang et al, 2021a ; Zhou et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

Identification of COVID-19-Specific Immune Markers Using a Machine Learning Method

Huang

Liao

et al. 2022

Front. Mol. Biosci.

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Notably, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a tight relationship with the immune system. Human resistance to COVID-19 infection comprises two stages. The first stage is immune defense, while the second stage is extensive inflammation. This process is further divided into innate and adaptive immunity during the immune defense phase. These two stages involve various immune cells, including CD4+ T cells, CD8+ T cells, monocytes, dendritic cells, B cells, and natural killer cells. Various immune cells are involved and make up the complex and unique immune system response to COVID-19, providing characteristics that set it apart from other respiratory infectious diseases. In the present study, we identified cell markers for differentiating COVID-19 from common inflammatory responses, non-COVID-19 severe respiratory diseases, and healthy populations based on single-cell profiling of the gene expression of six immune cell types by using Boruta and mRMR feature selection methods. Some features such as IFI44L in B cells, S100A8 in monocytes, and NCR2 in natural killer cells are involved in the innate immune response of COVID-19. Other features such as ZFP36L2 in CD4+ T cells can regulate the inflammatory process of COVID-19. Subsequently, the IFS method was used to determine the best feature subsets and classifiers in the six immune cell types for two classification algorithms. Furthermore, we established the quantitative rules used to distinguish the disease status. The results of this study can provide theoretical support for a more in-depth investigation of COVID-19 pathogenesis and intervention strategies.

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Identification of Cell Markers and Their Expression Patterns in Skin Based on Single-Cell RNA-Sequencing Profiles

Cited by 24 publications

References 83 publications

Detecting Brain Structure-Specific Methylation Signatures and Rules for Alzheimer’s Disease

Detecting Brain Structure-Specific Methylation Signatures and Rules for Alzheimer’s Disease

Identifying COVID-19 Severity-Related SARS-CoV-2 Mutation Using a Machine Learning Method

Identification of COVID-19-Specific Immune Markers Using a Machine Learning Method

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