A New Machine Learning-Based Framework for Mapping Uncertainty Analysis in RNA-Seq Read Alignment and Gene Expression Estimation

A New Machine Learning-Based Framework for Mapping Uncertainty Analysis in RNA-Seq Read Alignment and Gene Expression Estimation

One of the main benefits of using modern RNA-Sequencing (RNA-Seq) technology is the more accurate gene expression estimations compared with previous generations of expression data, such as the microarray. However, numerous issues can result in the possibility that an RNA-Seq read can be mapped to mu...

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Bibliographic Details
Main Authors: Adam McDermaid, Xin Chen, Yiran Zhang, Cankun Wang, Shaopeng Gu, Juan Xie, Qin Ma
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-08-01
Series:Frontiers in Genetics
Subjects:
gene expression
RNA-Seq read alignment
mapping uncertainty
machine learning
elastic-net
mixture model fitting
Online Access:https://www.frontiersin.org/article/10.3389/fgene.2018.00313/full

Internet

https://www.frontiersin.org/article/10.3389/fgene.2018.00313/full

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