Inferential literacy for experimental high-throughput biology

被引：23

作者：

Miron, M

Nadon, R

机构：

[1] McGill Univ, Montreal, PQ H3A 1A4, Canada

[2] Genome Quebec Innovat Ctr, Montreal, PQ H3A 1A4, Canada

[3] McGill Univ, Dept Human Genet, Montreal, PQ H3A 1B1, Canada

来源：

TRENDS IN GENETICS | 2006年 / 22卷 / 02期

关键词：

D O I：

10.1016/j.tig.2005.12.001

中图分类号：

Q3 [遗传学];

学科分类号：

071007 ; 090102 ;

摘要：

Many biologists believe that data analysis expertise lags behind the capacity for producing high-throughput data. One view within the bioinformatics community is that biological scientists need to develop algorithmic skills to meet the demands of the new technologies. In this article, we argue that the broader concept of inferential literacy, which includes understanding of data characteristics, experimental design and statistical analysis, in addition to computation, more adequately encompasses what is needed for efficient progress in high-throughput biology.

引用

页码：84 / 89

页数：6

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