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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Jul 20, 2025

iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution

iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution

Published on: April 30, 2011

CL-Impute:一种基于学习的对比性归算,用于退出单细胞RNA-seq数据.

Yuchen Shi¹, Jian Wan², Xin Zhang¹

¹School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China; Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education, Ministry of Education, China.

Computers in biology and medicine

|August 2, 2023

概括

在单细胞RNA测序 (scRNA-seq) 中,CL-Impute有效地通过使用对比学习来克服学事件来赋予缺失的基因数据. 这种方法提高了scRNA-seq分析的可靠性,即使失业率很高.

关键词:

相反的学习学习.下游分析下游分析退学事件的发生.计入计算是指计入计算的方法.这就是scRNA-seqq.

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Last Updated: Jul 20, 2025

iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution

iCLIP - Transcriptome-wide Mapping of Protein-RNA Interactions with Individual Nucleotide Resolution

Published on: April 30, 2011

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科学领域:

基因组学就是基因组学.
计算生物学计算生物学
生物信息学是一种生物信息学.

背景情况:

单细胞RNA测序 (scRNA-seq) 能够进行详细的细胞异质性研究.
在scRNA-seq数据中的脱落事件损害了下游分析的可靠性.
目前的归算方法与来自噪音数据的不准确的细胞关系作斗争.

研究的目的:

为scRNA-seq数据开发一种新的归算方法,以应对失学事件带来的挑战.
为了提高scRNA-seq数据集中的基因表达赋值的准确性和可靠性.
克服依赖潜在不值得信赖的细胞关系的现有方法的局限性.

主要方法:

拟议的CL-Impute (基于对比学习的Impute) 模型.
利用对比式学习来学习从学事件中获得的细胞表示.
采用自我注意网络来捕捉全球细胞关系.

主要成果:

在定量评估,细胞聚类,基因识别和轨迹推断方面,CL-Impute在最先进的方法中表现优越.
对比学习和掩盖细胞增强的结合使得从噪音数据中学习实际潜伏特征成为可能.
提高了scRNA-seq数据中计算值的可靠性,其中断率很高.

结论:

CL-Impute是一种有效的基于对比式学习的方法,用于赋值scRNA-seq数据,特别是在高脱落场景中.
该方法通过专注于放弃事件和全球细胞相互作用来学习强大的细胞表征.
对CL-Impute模型的源代码是可用的,这有助于其采用和进一步研究.