Machine-learning approaches for classifying haplogroup from Y chromosome STR data

PLoS Computational Biology

Volumn 4, Issue 6, 2008, Pages

  Schlecht, Joseph ^a   Kaplan, Matthew E ^b   Barnard, Kobus ^a   Karafet, Tatiana ^b   Hammer, Michael F ^b   Merchant, Nirav C ^b  

^a University of Arizona   (United States)

^b UNIVERSITY OF ARIZONA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; GENES; LEARNING ALGORITHMS; LEARNING SYSTEMS; STATISTICAL TESTS;

GENETIC VARIATION; GENOTYPING; GEOGRAPHIC STRUCTURES; HAPLOGROUP; LOW RATES; MACHINE LEARNING APPROACHES; MODERN MACHINES; SHORT TANDEM REPEATS; SINGLE NUCLEOTIDE POLYMORPHISMS; Y CHROMOSOMES;

COST EFFECTIVENESS;

ACCURACY; ARTICLE; GENE FREQUENCY; GENETIC VARIABILITY; GENOTYPE; HAPLOTYPE; LEARNING ALGORITHM; MACHINE LEARNING; MATHEMATICAL MODEL; SCORING SYSTEM; SHORT TANDEM REPEAT; SINGLE NUCLEOTIDE POLYMORPHISM; Y CHROMOSOME; ALGORITHM; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; CHROMOSOME MAP; DNA SEQUENCE; GENETICS; HUMAN; METHODOLOGY; MOLECULAR EVOLUTION; MUTATION; NUCLEOTIDE SEQUENCE;

MICROSATELLITE DNA;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CHROMOSOME MAPPING; CHROMOSOMES, HUMAN, Y; DNA MUTATIONAL ANALYSIS; EVOLUTION, MOLECULAR; HAPLOTYPES; HUMANS; MICROSATELLITE REPEATS; MUTATION; PATTERN RECOGNITION, AUTOMATED; POLYMORPHISM, SINGLE NUCLEOTIDE; SEQUENCE ANALYSIS, DNA; VARIATION (GENETICS);

EID: 46249132158     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1000093     Document Type: Article

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