The regulatory role of the Micro-RNAs (miRNAs) in the messenger RNAs (mRNAs) gene expression is well understood by the biologists since some decades, even though the delving into specific aspects is in progress. Clust...The regulatory role of the Micro-RNAs (miRNAs) in the messenger RNAs (mRNAs) gene expression is well understood by the biologists since some decades, even though the delving into specific aspects is in progress. Clustering is a cornerstone in bioinformatics research, offering a potent computational tool for analyzing diverse types of data encountered in genomics and related fields. MiRNA clustering plays a pivotal role in deciphering the intricate regulatory roles of miRNAs in biological systems. It uncovers novel biomarkers for disease diagnosis and prognosis and advances our understanding of gene regulatory networks and pathways implicated in health and disease, as well as drug discovery. Namely, we have implemented clustering procedure to find interrelations among miRNAs within clusters, and their relations to diseases. Deep clustering (DC) algorithms signify a departure from traditional clustering methods towards more sophisticated techniques, that can uncover intricate patterns and relationships within gene expression data. Deep learning (DL) models have shown remarkable success in various domains, and their application in genomics, especially for tasks like clustering, holding immense promise. The deep convolutional clustering procedure used is different from other traditional methods, demonstrating unbiased clustering results. In the paper, we implement the procedure on a Multiple Myeloma miRNA dataset publicly available on GEO platform, as a template of a cancer instance analysis, and hazard some biological issues.展开更多
以Scopus数据库和Web of Science数据共同收录的图书情报学领域的38种期刊近10年所刊载的研究论文为数据源。利用共词聚类分析获得各个研究主题类团。基于关键词及其在文献中的共现情况,构建词共现关键词网络。并利用社会网络分析方法...以Scopus数据库和Web of Science数据共同收录的图书情报学领域的38种期刊近10年所刊载的研究论文为数据源。利用共词聚类分析获得各个研究主题类团。基于关键词及其在文献中的共现情况,构建词共现关键词网络。并利用社会网络分析方法从网络中心性角度分析每个关键词在网络中的地位,以及它们之间的联系,揭示研究主题的结构特征。最后通过二维战略坐标图识别不同主题类团之间的演变趋势。展开更多
文摘The regulatory role of the Micro-RNAs (miRNAs) in the messenger RNAs (mRNAs) gene expression is well understood by the biologists since some decades, even though the delving into specific aspects is in progress. Clustering is a cornerstone in bioinformatics research, offering a potent computational tool for analyzing diverse types of data encountered in genomics and related fields. MiRNA clustering plays a pivotal role in deciphering the intricate regulatory roles of miRNAs in biological systems. It uncovers novel biomarkers for disease diagnosis and prognosis and advances our understanding of gene regulatory networks and pathways implicated in health and disease, as well as drug discovery. Namely, we have implemented clustering procedure to find interrelations among miRNAs within clusters, and their relations to diseases. Deep clustering (DC) algorithms signify a departure from traditional clustering methods towards more sophisticated techniques, that can uncover intricate patterns and relationships within gene expression data. Deep learning (DL) models have shown remarkable success in various domains, and their application in genomics, especially for tasks like clustering, holding immense promise. The deep convolutional clustering procedure used is different from other traditional methods, demonstrating unbiased clustering results. In the paper, we implement the procedure on a Multiple Myeloma miRNA dataset publicly available on GEO platform, as a template of a cancer instance analysis, and hazard some biological issues.
文摘以Scopus数据库和Web of Science数据共同收录的图书情报学领域的38种期刊近10年所刊载的研究论文为数据源。利用共词聚类分析获得各个研究主题类团。基于关键词及其在文献中的共现情况,构建词共现关键词网络。并利用社会网络分析方法从网络中心性角度分析每个关键词在网络中的地位,以及它们之间的联系,揭示研究主题的结构特征。最后通过二维战略坐标图识别不同主题类团之间的演变趋势。
