Graphics processing unit implementations of relative expression analysis algorithms enable dramatic computational speedup

被引：7
作者：
Magis, Andrew T. [1 ]
Earls, John C. [2 ]
Ko, Youn-Hee [2 ]
Eddy, James A. [3 ]
Price, Nathan D. [1 ,4 ]
机构：
[1] Univ Illinois, Ctr Biophys & Computat Biol, Urbana, IL 61801 USA
[2] Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA
[3] Univ Illinois, Dept Bioengn, Urbana, IL 61801 USA
[4] Univ Illinois, Dept Chem & Biomol Engn, Urbana, IL 61801 USA
来源：
BIOINFORMATICS | 2011年 / 27卷 / 06期
基金：
美国国家卫生研究院;
关键词：
CLASSIFICATION; CANCERS; CELL;
D O I：
10.1093/bioinformatics/btr033
中图分类号：
Q5 [生物化学];
学科分类号：
071010 ; 081704 ;
摘要：
SUMMARY: The top-scoring pair (TSP) and top-scoring triplet (TST) algorithms are powerful methods for classification from expression data, but analysis of all combinations across thousands of human transcriptome samples is computationally intensive, and has not yet been achieved for TST. Implementation of these algorithms for the graphics processing unit results in dramatic speedup of two orders of magnitude, greatly increasing the searchable combinations and accelerating the pace of discovery. AVAILABILITY: http://www.igb.illinois.edu/labs/price/downloads/. CONTACT: ndprice@illinois.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
引用
收藏
页码：872 / 873
页数：2
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