دانلود مقاله انگلیسی رایگان:طبقه بندی بدون نظارت شده داده های چند omics در طی بازسازی قلب با استفاده از یادگیری عمیق - 2019
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  • Unsupervised classification of multi-omics data during cardiac remodeling using deep learning Unsupervised classification of multi-omics data during cardiac remodeling using deep learning
    Unsupervised classification of multi-omics data during cardiac remodeling using deep learning

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Unsupervised classification of multi-omics data during cardiac remodeling using deep learning


    ترجمه فارسی عنوان مقاله:

    طبقه بندی بدون نظارت شده داده های چند omics در طی بازسازی قلب با استفاده از یادگیری عمیق


    منبع:

    Sciencedirect - Elsevier - Methods, 166 (2019) 66-73: doi:10:1016/j:ymeth:2019:03:004


    نویسنده:

    Neo Christopher Chunga,b,1,⁎, Bilal Mirzaa,c,1, Howard Choia,c,f, Jie Wanga,c, Ding Wanga,c, Peipei Pinga,c,e,f,g, Wei Wanga,d,


    چکیده انگلیسی:

    Integration of multi-omics in cardiovascular diseases (CVDs) presents high potentials for translational discoveries. By analyzing abundance levels of heterogeneous molecules over time, we may uncover biological interactions and networks that were previously unidentifiable. However, to effectively perform integrative analysis of temporal multi-omics, computational methods must account for the heterogeneity and complexity in the data. To this end, we performed unsupervised classification of proteins and metabolites in mice during cardiac remodeling using two innovative deep learning (DL) approaches. First, long short-term memory (LSTM)- based variational autoencoder (LSTM-VAE) was trained on time-series numeric data. The low-dimensional embeddings extracted from LSTM-VAE were then used for clustering. Second, deep convolutional embedded clustering (DCEC) was applied on images of temporal trends. Instead of a two-step procedure, DCEC performes a joint optimization for image reconstruction and cluster assignment. Additionally, we performed K-means clustering, partitioning around medoids (PAM), and hierarchical clustering. Pathway enrichment analysis using the Reactome knowledgebase demonstrated that DL methods yielded higher numbers of significant biological pathways than conventional clustering algorithms. In particular, DCEC resulted in the highest number of enriched pathways, suggesting the strength of its unified framework based on visual similarities. Overall, unsupervised DL is shown to be a promising analytical approach for integrative analysis of temporal multi-omics.
    Keywords: Cardiovascular | Clustering | Multi-omics Time-series | Unsupervised deep learning | Integrative analysis


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 8
    حجم فایل: 1427 کیلوبایت

    قیمت: رایگان


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