Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

A multiobjective approach to the genetic code adaptability problem.

BMC bioinformatics - 19 Feb 2015

de Oliveira Lariza Laura, de Oliveira Paulo S L, Tinós Renato

Abstract excerpt

BACKGROUND: The organization of the canonical code has intrigued researches since it was first described. If we consider all codes mapping the 64 codes into 20 amino acids and one stop codon, there are more than 1.51×10(84) possible genetic codes. The main question related to the organization of the genetic code is why exactly the canonical code was selected among this huge number of possible genetic codes. Many...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Amino Acids
Share to Evolution, Molecular

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to Genetic Code

Share to Models, Genetic

Share to Mutation

Share to Protein Biosynthesis

Share to Selection, Genetic

Source

Open in PubMed Open full text

Identifiers

PMID: 25879480
DOI: 10.1186/s12859-015-0480-9
PMCID: PMC4341243

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

A multiobjective approach to the genetic code adaptability problem.PMID 25879480Read ↗

The role of crossover operator in evolutionary-based approach to the problem of genetic code optimization.Semantic proximity Read ↗

Study of the genetic code adaptability by means of a genetic algorithm.Semantic proximity Read ↗

Designing a neural network for the constraint optimization of the fitness functions devised based on the load minimization of the genetic code.Semantic proximity Read ↗

Analysis of the optimality of the standard genetic code.Semantic proximity Read ↗

Coding rules for amino acids in the genetic code: the genetic code is a minimal code of mutational deterioration.Semantic proximity Read ↗

A genetic engineering approach to genetic algorithms.Semantic proximity Read ↗

A binary representation of the genetic code.Semantic proximity Read ↗

Local stability and evolution of the genetic code.Semantic proximity Read ↗

Optimization models and the structure of the genetic code.Semantic proximity Read ↗

Objective functions.Semantic proximity Read ↗

From amino acid landscape to protein landscape: analysis of genetic codes in terms of fitness landscape.Semantic proximity Read ↗

Empirical analysis of locality, heritability and heuristic bias in evolutionary algorithms: a case study for the multidimensional knapsack problem.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.