An evolutionary framework for measuring epigenomic information and estimating cell-type-specific fitness consequences (vol 51, pg 335, 2019)

被引：0
|
作者：
Gulko, Brad
Siepel, Adam
机构：
[1] Cornell University,Graduate Field of Computer Science
[2] Cold Spring Harbor Laboratory,Simons Center for Quantitative Biology
来源：
NATURE GENETICS | 2019年 / 51卷 / 03期
关键词：
D O I：
10.1038/s41588-019-0369-z
中图分类号：
Q3 [遗传学];
学科分类号：
071007 ; 090102 ;
摘要：
In the version of this article initially published, in the Methods section ‘Statistics and data analysis’, subsection ‘Measuring entropy with INSIGHT’, the equation for the maximized log likelihood incorrectly duplicated the equation for entropy; the equation read but should have read. The error has been corrected in the HTML and PDF versions of the article.
引用
收藏
页码：577 / 577
页数：1
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  • [3] Publisher Correction: An evolutionary framework for measuring epigenomic information and estimating cell-type-specific fitness consequences
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