Results for genotype

genotype provides an automated pipipeline for comparinggenotypes from different assays.

An abstraction layer between genotype and phenotype, with in-place mutation.

Genotype language CLI

Rust-accelerated population genetics toolkit with ergonomic Python bindings

A genetic algorithm implementation

Genotype language config crate

Genotype language JSON tree converter crate

Genotype language target config crate

Genotype language target project crate

Genotype language target crate

Genotype language Python target config crate

Genotype language Python target project crate

Genotype language Python target AST crate

A Ruby Gem used for calculating a consensus (most frequent) DNA sequence from an array of ALIGNED sequences. Useful for bioinformatics pipelines to create a reference sequence when using another sequence aligning utility. Also, helpful in finding consensus regions for primer design or viral genotyping.

== DESCRIPTION: Charlie is a library for genetic algorithms (GA) and genetic programming (GP). == FEATURES: - Quickly develop GAs by combining several parts (genotype, selection, crossover, mutation) provided by the library. - Sensible defaults are provided with any genotype, so often you only need to define a fitness function. - Easily replace any of the parts by your own code. - Test different strategies in GA, and generate reports comparing them. Example report: http://charlie.rubyforge.org/example_report.html == INSTALL: * sudo gem install charlie == EXAMPLES: This example solves a TSP problem (also quiz #142): N=5 CITIES = (0...N).map{|i| (0...N).map{|j| [i,j] } }.inject{|a,b|a+b} class TSP < PermutationGenotype(CITIES.size) def fitness d=0 (genes + [genes[0]]).each_cons(2){|a,b| a,b=CITIES[a],CITIES[b] d += Math.sqrt( (a[0]-b[0])**2 + (a[1]-b[1])**2 ) } -d # lower distance -> higher fitness. end use EdgeRecombinationCrossover, InversionMutator end Population.new(TSP,20).evolve_on_console(50) This example finds a polynomial which approximates cos(x) class Cos < TreeGenotype([proc{3*rand-1.5},:x], [:-@], [:+,:*,:-]) def fitness -[0,0.33,0.66,1].map{|x| (eval_genes(:x=>x) - Math.cos(x)).abs }.max end use TournamentSelection(4) end Population.new(Cos).evolve_on_console(500)

DEPRECATED PROJECT. MIGRATED TO PYTHON: https://github.com/seoanezonjic/pets. PETS suite includes three different tools. CohortAnalyzer performs the calculation of several statistics that gives an overview of a cohort of patients to analyse. Reg2Phen uses associations between pathological phenotypes and regions of the genome (these associations can be calculated from the cohort of patients if they include genotypic & phenotypic information using NetAnalyzer, another Ruby gem) to find, for a given genomic region, which pathological phenotypes have been associated with that region. The third tool, Phen2Reg, is a predictor that using the same associations as Reg2Phen, predicts which genomic regions can be the cause of a list of pathological phenotypes observed in a patient.

No description provided.

No description provided.

No description provided.

No description provided.

No description provided.