ReviewBioengineered
Naturally selecting solutions: the use of genetic algorithms in bioinformatics.
Review in Bioengineered. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Computational design and evaluation of optimal bait sets for scalable proximity proteomics.Nature communications · 2025Article
- GArNet: A Genetic Algorithm-Based Protein Redesign Approach to Optimize Mutation Combinations Informed by Network Theory.Journal of chemical information and modeling · 2025Article
- GA-GBLUP: leveraging the genetic algorithm to improve the predictability of genomic selection.Briefings in bioinformatics · 2024Article
- A transcriptomic study for identifying cardia- and non-cardia-specific gastric cancer prognostic factors using genetic algorithm-based methods.Journal of cellular and molecular medicine · 2020Article
- The superior fault tolerance of artificial neural network training with a fault/noise injection-based genetic algorithm.Protein & cell · 2016Article
- Under the microscope: From pathogens to probiotics and back.Bioengineered · 2015Review
- Membrane protein orientation and refinement using a knowledge-based statistical potential.BMC bioinformatics · 2013Article
- A beginner's guide to phylogenetics.Microbial ecology · 2013Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.