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A Bayesian approach to accurate and robust signature detection on LINCS L1000 data.一种贝叶斯方法,用于在 LINCS L1000 数据上进行准确和稳健的特征检测。
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A learning-based framework for miRNA-disease association identification using neural networks.基于神经网络的 miRNA-疾病关联识别学习框架。
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从单细胞 RNA 序列构建细胞特异性基因关联网络。

Cell-specific gene association network construction from single-cell RNA sequence.

机构信息

College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, P.R. China.

出版信息

Cell Cycle. 2021 Nov;20(21):2248-2263. doi: 10.1080/15384101.2021.1978265. Epub 2021 Sep 16.

DOI:10.1080/15384101.2021.1978265
PMID:34530677
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8794512/
Abstract

The recent development of a high throughput single-cell RNA sequence devises the opportunity to study entire transcriptomes in the smallest detail. It also leads to the characterization of molecules and subtypes of a cell. Cancer epigenetics induced not only from individual molecules but also from the dysfunction of the system and the coupling effect of genes. While rapid advances are being made in the development of tools for single-cell RNA-seq data analysis, few slants are noticed in the potential advantages of single-cell network construction.Here, we used network perturbation theory with significant analysis to develop a cell-specific network that provides an insight into gene-gene association based on molecular expressions in a single-cell resolution. Besides, using this method, we can characterize each cell by inspecting how genes are connected and can identify the hub genes using network degree theory. Pathway & Gene enrichment analysis of the identified cell-specific high network degree genes supported the effectiveness of this method. This method could be beneficial for personalized drug design and even therapeutics.

摘要

高通量单细胞 RNA 测序技术的最新发展为研究整个转录组提供了更详细的机会。它还可以对细胞的分子和亚型进行特征描述。癌症表观遗传学不仅受到单个分子的诱导,而且还受到系统功能障碍和基因偶联效应的影响。虽然单细胞 RNA-seq 数据分析工具的发展取得了快速进展,但单细胞网络构建的潜在优势却很少被注意到。在这里,我们使用具有显著分析的网络干扰理论来开发一种细胞特异性网络,该网络提供了基于单细胞分辨率的分子表达的基因-基因关联的深入见解。此外,使用这种方法,我们可以通过检查基因如何连接来描述每个细胞,并使用网络度理论识别枢纽基因。鉴定出的细胞特异性高网络度基因的通路和基因富集分析支持了这种方法的有效性。这种方法可能对个性化药物设计甚至治疗有帮助。