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A Gene Expression Clustering Method to Extraction of Cell-to-Cell Biological Communication

 
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<title>A Gene Expression Clustering Method to Extraction of Cell-to-Cell Biological Communication</title>
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<name type="personal" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20220004770">
<namePart>Wang, Hui</namePart>
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<abstract displayLabel="Summary">Graph-based clustering identification is a practical method to detect the communication between nodes in complex networks that has obtained considerable comments. Since identifying different communities in large-scale data is a challenging task, by understanding the communication between the behaviors of the elements in a community (a cluster), the general characteristics of clusters can be predicted. Graph-based clustering methods have played an important role in clustering gene expression data because of their ability to show the relations between the data. In order to be able to identify genes that lead to the development of diseases, the communication between the cells must be established. The communication between different cells can be indicated by the expression of different genes within them. In this study, the problem of cell-to-cell communication is expressed as a graph and the communication are extracted by recognizing the communities. The FANTOM5 dataset is used to simulate and calculate the similarity between cells. After preprocessing and normalizing the data, to convert this data into graphs, the expression of genes in different cells was examined and by considering a threshold and Wilcoxon test, the communication between them were identified through using clustering.

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<accessCondition type="use and reproduction">La copia digital se distribuye bajo licencia "Attribution 4.0 International (CC BY NC 4.0)"</accessCondition>
<note type="statement of responsibility">Hui Wang...[et.al.]</note>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080611200">
<topic>Inteligencia artificial</topic>
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<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20210016516">
<topic>Extracción selectiva</topic>
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<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080547424">
<topic>Genética</topic>
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<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20210026096">
<topic>Transdiferenciación celular</topic>
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<title>Revista Iberoamericana de Inteligencia Artificial</title>
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<publisher> : IBERAMIA, Sociedad Iberoamericana de Inteligencia Artificial , 2018-</publisher>
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<identifier type="issn">1988-3064</identifier>
<identifier type="local">MAP20200034445</identifier>
<part>
<text>02/05/2022 Volumen 25 Número 69 - mayo 2022 , p. 1-12</text>
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