0000000000219341

AUTHOR

Mark Robinson

0000-0002-1520-8459

showing 2 related works from this author

Reconstructing the deep population history of Central and South America

2018

We report genome-wide ancient DNA from 49 individuals forming four parallel time transects in Belize, Brazil, the Central Andes, and the Southern Cone, each dating to at least ∼9,000 years ago. The common ancestral population radiated rapidly from just one of the two early branches that contributed to Native Americans today. We document two previously unappreciated streams of gene flow between North and South America. One affected the Central Andes by ∼4,200 years ago, while the other explains an affinity between the oldest North American genome associated with the Clovis culture and the oldest Central and South Americans from Chile, Brazil, and Belize. However, this was not the primary sou…

0301 basic medicineGene Flow010506 paleontologyHistoryPopulationPopulationPopulation ReplacementBiology01 natural sciencesGenomeMedical and Health SciencesDNA MitochondrialGeneral Biochemistry Genetics and Molecular BiologyGene flowAncient03 medical and health sciencesTheoreticalModelsGeneticsHumansGENÉTICA DE POPULAÇÕESanthropologyIndis de l'Amèrica CentralDNA AncientTransecteducationHistory Ancient0105 earth and related environmental scienceseducation.field_of_studypopulation geneticGenomeGenome HumanHuman Genomepopulation geneticsarchaeologyCentral AmericaDNABiological SciencesSouth AmericaModels TheoreticalArchaeologyMitochondrial030104 developmental biologyAncient DNAGenetics PopulationDevelopmental BiologyHuman
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Integrating genomic binding site predictions using real-valued meta classifiers

2008

Currently the best algorithms for predicting transcription factor binding sites in DNA sequences are severely limited in accuracy. There is good reason to believe that predictions from different classes of algorithms could be used in conjunction to improve the quality of predictions. In this paper, we apply single layer networks, rules sets, support vector machines and the Adaboost algorithm to predictions from 12 key real valued algorithms. Furthermore, we use a ‘window’ of consecutive results as the input vector in order to contextualise the neighbouring results. We improve the classification result with the aid of under- and over-sampling techniques. We find that support vector machines …

Artificial neural networkComputer sciencebusiness.industryMachine learningcomputer.software_genreDNA binding siteSupport vector machineArtificial IntelligenceArtificial intelligenceAdaBoostPrecision and recallbusinessClassifier (UML)computerSoftwareNeural Computing and Applications
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