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Data from: Controlling meiotic recombinational repair: specifying the roles of ZMMs, Sgs1 and Mus81/Mms4 in crossover formation
负责人:
关键词:
meiosis;recombination;ReCombine;Saccharomyces cerevisiae
DOI:
doi:10.5061/dryad.79hn1
摘要:
Crossovers (COs) play a critical role in ensuring proper alignment and segregation of homologous chromosomes during meiosis. How the cell bal
Data from: Climate, demography, and zoogeography predict introgression thresholds in Salmonid hybrid zones in Rocky Mountain streams
负责人:
关键词:
hybrid swarm;Trout;Oncorhynchus clarkii;1984-2016;hybridization;Oncorhynchus mykiss;introgression;climate change
DOI:
doi:10.5061/dryad.73s0n
摘要:
; classification success, 72–82%; 10-fold cross validation, 70–82%) and predicted that rainbow trout introgression was significantly associated with warmer
Data from: Comparing the prediction of joint species distribution models with respect to characteristics of sampling data
负责人:
关键词:
JSDM;cross validation;sampling
DOI:
doi:10.5061/dryad.rd55f
摘要:
owed inconsistent performances between the goodness-of-fit and predictability in cross-validation, and the Boral model was relatively robust than others
Data from: Frequency and distribution of 152 genetic disease variants in over 100,000 mixed breed and purebred dogs
负责人:
Donner, Jonas
关键词:
dog canine Mendelian disease genotypes panel screening
DOI:
doi:10.5061/dryad.dd91b
摘要:
on in the generated data. We identified the most prevalent, and rare, disease susceptibility variants across the general dog population while providing the fir
Data from: TipDatingBeast: an R package to assist the implementation of phylogenetic tip-dating tests using BEAST
负责人:
关键词:
phylogenetic tip-dating;Phylogenetic Theory and Methods;phylogeography;molecular evolution;Bioinfomatics\/Phyloinfomatics;Measurably-evolving populations (MEPs);BEAST;date-randomization test;leave-one-out cross-validation
DOI:
doi:10.5061/dryad.43q71
摘要:
the temporal signal of a dataset. The second function allows performing leave-one-out analyses, which test for the consistency between independent calibration
Data from: Enhancing genomic prediction with genome-wide association studies in multiparental maize populations
负责人:
关键词:
Genomic prediction;Zea mays;Genome-wide association study
DOI:
doi:10.5061/dryad.cd3hv
摘要:
of this joint approach depends on the extent of polygenicity of the traits. Congruent with this finding, cross-validation studies indicated that GP including
validation
负责人:
关键词:
Aquaculture Conservation Genetics Fisheries Management Population Genetics - Empirical
DOI:
doi:10.5061/dryad.4nf1c
摘要:
cs and structure analyses revealed potential shared ancestry and admixture between populations. We utilized the Sequenom MassARRAY to validate two multiplex panels
Data from: A clinical decision support system learned from data to personalize treatment recommendations towards preventing breast cancer metastasis
负责人:
关键词:
treatment;clinical;metastasis;Decision Support;Bayesian network;Breast Cancer;electronic health records;Holocene;EHR;H. sapiens
DOI:
doi:10.5061/dryad.64964m0
摘要:
r breast cancer metastasis. Results: In a 5-fold cross-validation analysis, we compared the probability of being metastasis free in 5 years for patients who mad
Data from: Assessing the expected response to genomic selection of individuals and families in Eucalyptus breeding with an additive-dominant model
负责人:
关键词:
Eucalyptus grandis;growth traits;SNPs;Eucalyptus urophylla;GBLUP;Tree breeding;Genomic prediction;wood traits
DOI:
doi:10.5061/dryad.ms580
摘要:
ons of genomically selected individuals, using a rigorous cross-validation (CV) scheme that removed relatedness between training and validation sets. Predicti
; Simoes, Rolf; Carvalho, Alexandre X Y; Maciel, Adeline; Coutinho, Alexandre; Esquerdo, Julio; Antunes, Joao; Begotti, Rodrigo; Arvor, Damien; Almeida
负责人:
关键词:
File content File name File format File size Uniform resource locator\/link to file Multiple investigations
DOI:
doi:10.1594/pangaea.881291
摘要:
ween crop and pasture expansion. Quality assessment using a 5-fold cross-validation of the training samples indicates an overall accuracy of 93

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