Naturally Selecting Solutions: the Use of Genetic Algorithms in Bioinformatics

Overview

Journal Bioengineered

Date 2012 Dec 11

PMID 23222169

Citations 7

Authors

Timmy Manning

Roy D Sleator

Paul Walsh

Affiliations

Soon will be listed here.

Abstract

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.

Citing Articles

GA-GBLUP: leveraging the genetic algorithm to improve the predictability of genomic selection.

Xu Y, Zhang Y, Cui Y, Zhou K, Yu G, Yang W Brief Bioinform. 2024; 25(5).

PMID: 39101500 PMC: 11299030. DOI: 10.1093/bib/bbae385.

A transcriptomic study for identifying cardia- and non-cardia-specific gastric cancer prognostic factors using genetic algorithm-based methods.

Xin J, Wu Y, Wang X, Li S, Chu H, Wang M J Cell Mol Med. 2020; 24(16):9457-9465.

PMID: 32649057 PMC: 7417703. DOI: 10.1111/jcmm.15618.

The superior fault tolerance of artificial neural network training with a fault/noise injection-based genetic algorithm.

Su F, Yuan P, Wang Y, Zhang C Protein Cell. 2016; 7(10):735-748.

PMID: 27502185 PMC: 5055486. DOI: 10.1007/s13238-016-0302-5.

Under the microscope: From pathogens to probiotics and back.

Sleator R Bioengineered. 2015; 6(5):275-82.

PMID: 26339986 PMC: 4825842. DOI: 10.1080/21655979.2015.1089368.

Biologically inspired intelligent decision making: a commentary on the use of artificial neural networks in bioinformatics.

Manning T, Sleator R, Walsh P Bioengineered. 2013; 5(2):80-95.

PMID: 24335433 PMC: 4049912. DOI: 10.4161/bioe.26997.

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