Supplementary table S1

Figshare2021-10-29 更新2026-04-28 收录

转录因子预测

SNP单核苷酸多态性

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Overview of PWMs and their performance in recognizing SNPs affecting transcription factor binding in SNP-SELEX data.

创建时间：

2021-10-29

Table_1_Genomic Prediction of Breeding Values Using a Subset of SNPs Identified by Three Machine Learning Methods.pdf

基因组育种值预测

SNP单核苷酸多态性

The analysis of large genomic data is hampered by issues such as a small number of observations and a large number of predictive variables (commonly known as “large P small N”), high dimensionality or

NIAID Data Ecosystem50

Additional file 20: of Bacterial whole genome-based phylogeny: construction of a new benchmarking dataset and assessment of some existing methods

细菌基因组系统发育

SNP单核苷酸多态性

Position for mutations from S19 SNP alignment. Positions related to NC000913. (TXT 5 kb)

NIAID Data Ecosystem40

Average AUC in cross-classification experiments.

转录因子预测

交叉分类性能评估

Table 6 indicates the average AUC score across ten classifiers where training data from one TF (rows) was used to train a classifier that classified TFBS for another TF (columns).

Figshare2015-12-02 更新30

Frequency of deleterious coding and noncoding SNPs.

SNP单核苷酸多态性

基因变异功能分析

The alignments and data are the same as those used in Table 4. The number of deleterious SNPs is the difference between the total and neutral number of SNPs. The number of neutral nonsynonymous SNPs i

NIAID Data Ecosystem50

33Grapes.Total.SNP.final.MT.vcf.gz

葡萄遗传多样性

SNP单核苷酸多态性

Candidate SNP variants detected by analysis of genome resequencing data from 33 grape accessions

Figshare2020-05-13 更新30