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Whole-genome resequencing of big-fruited tomato strains for QTL mapping and genomic prediction of agronomically important traits
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创建时间：
2023-03-01
相关数据集
Data from: The impact of variable degrees of freedom and scale parameters in Bayesian methods for genomic prediction in Chinese Simmental beef cattle
Three conventional Bayesian approaches (BayesA, BayesB and BayesCπ) have been demonstrated to be powerful in predicting genomic merit for complex traits in livestock. A priori, these Bayesian models
DataCite Commons2025-04-01 更新80
GPTransformer: A transformer-based deep learning method for predicting Fusarium related traits in barley.. GPTransformer: A transformer-based deep learning method for predicting Fusarium related traits in barley.
Fusarium head blight (FHB) incited by Fusarium graminearum Schwabe is a devastating disease of barley and other cereal crops worldwide. Fusarium head blight is associated with trichothecene mycotoxins
NIAID Data Ecosystem40
Data_Sheet_4_Using Local Convolutional Neural Networks for Genomic Prediction.CSV
The prediction of breeding values and phenotypes is of central importance for both livestock and crop breeding. In this study, we analyze the use of artificial neural networks (ANN) and, in particular
NIAID Data Ecosystem20
Performance on the VEST-indel test set for frameshift variations.
Performance on the VEST-indel test set for frameshift variations.
NIAID Data Ecosystem50
Table_4_Detecting the QTL-Allele System of Seed Oil Traits Using Multi-Locus Genome-Wide Association Analysis for Population Characterization and Optimal Cross Prediction in Soybean.XLSX
Soybean is one of the world's major vegetative oil sources, while oleic acid and linolenic acid content are the major quality traits of soybean oil. The restricted two-stage multi-locus genome-wide as
NIAID Data Ecosystem30
