Topic
Mutation (genetic algorithm)
About: Mutation (genetic algorithm) is a research topic. Over the lifetime, 31223 publications have been published within this topic receiving 720553 citations.
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TL;DR: In a survey of the spectrum of mutational burdens in 27 types of cancers, there was a correlation between an increased mutational burden and the response to checkpoint inhibition of PD-1 and PD-L1.
Abstract: In a survey of the spectrum of mutational burdens in 27 types of cancers, there was a correlation between an increased mutational burden and the response to checkpoint inhibition of PD-1 and PD-L1.
2,077 citations
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TL;DR: This work limits the definition of genetic structure to the nonrandom distribution of alleles or genotypes in space or time and disregard genome organization and meiotic processes that can also affect allele and genotype frequencies.
Abstract: Plant populations are not randomly arranged assemblages of genotypes but are structured in space and time (2, 29, 49, 58, 84, 112). This structure may be manifested among geographically distinct populations, within a local group of plants, or even in the progeny of individuals. Genetic structure results from the joint action of mutation, migration, selection, and drift, which in tum must operate within the historical and biological context of each plant species. Ecological factors affecting reproduction and dispersal are likely to be particularly important in determining genetic structure (2, 31, 58). Reproduction is the process that translates the current genotypic array into that of subsequent generations, while the dispersal of pollen and seeds determines the postreproductive pattems of gene dispersion within and among populations. Although the concept of genetic structure has been used in various ways (58, 130, 154), we limit our definition to the nonrandom distribution of alleles or genotypes in space or time and disregard genome organization and meiotic processes that can also affect allele and genotype frequencies. Because of the limited mobility of plants, their genetic structure implies spatial structure, or the actual physical distribution of individuals. While spatial pattems often have genetic implications, nonrandom genetic pattems can exist without a nonrandom distribution of individuals. Conversely, a population may have a nonrandom spatial distribution without any accompanying genetic structure. Spatial and genetic patterns are often assumed to result from environmental heterogeneity and differential selection pressures (22, 53, 131, 132). Selection is a ubiquitous feature of natural populations; it alters gene and
2,057 citations
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TL;DR: The risk of expansion during oogenesis to the full mutation associated with mental retardation increases with the number of repeats, and this variation in risk accounts for the Sherman paradox.
2,040 citations
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1,973 citations
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TL;DR: Gene expression programming, a genotype/phenotype genetic algorithm (linear and ramified), is presented here for the first time as a new technique for the creation of computer programs with high efficiency that greatly surpasses existing adaptive techniques.
Abstract: Gene expression programming, a genotype/phenotype genetic algorithm (linear and ramified), is presented here for the first time as a new technique for the creation of computer programs. Gene expression programming uses character linear chromosomes composed of genes structurally organized in a head and a tail. The chromosomes function as a genome and are subjected to modification by means of mutation, transposition, root transposition, gene transposition, gene recombination, and oneand two-point recombination. The chromosomes encode expression trees which are the object of selection. The creation of these separate entities (genome and expression tree) with distinct functions allows the algorithm to perform with high efficiency that greatly surpasses existing adaptive techniques. The suite of problems chosen to illustrate the power and versatility of gene expression programming includes symbolic regression, sequence induction with and without constant creation, block stacking, cellular automata rules for the density-classification problem, and two problems of boolean concept learning: the 11-multiplexer and the GP rule problem.
1,887 citations