Naturally selecting solutions The use of genetic algorithms in bioinformatics

被引:22
|
作者
Manning, Timmy [1 ]
Sleator, Roy D. [2 ]
Walsh, Paul [1 ]
机构
[1] Cork Inst Technol, Dept Comp Sci, Cork, Ireland
[2] Cork Inst Technol, Dept Biol Sci, Cork, Ireland
关键词
genetic algorithm; optimization; multiple sequence alignment; protein structure prediction; MULTIPLE SEQUENCE ALIGNMENT; TRAVELING SALESMAN PROBLEM; HYBRID; MODEL; SENSITIVITY; SEARCH; TOOLS; GAP;
D O I
10.4161/bioe.23041
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
For decades, computer scientists have looked to nature for biologically inspired solutions to computational problems; ranging from robotic control to scheduling optimization. Paradoxically, as we move deeper into the post-genomics era, the reverse is occurring, as biologists and bioinformaticians look to computational techniques, to solve a variety of biological problems. One of the most common biologically inspired techniques are genetic algorithms (GAs), which take the Darwinian concept of natural selection as the driving force behind systems for solving real world problems, including those in the bioinformatics domain. Herein, we provide an overview of genetic algorithms and survey some of the most recent applications of this approach to bioinformatics based problems.
引用
收藏
页码:266 / 278
页数:13
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