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QBiC-Pred: Quantitative Predictions of Transcription Factor Binding Changes Due to Sequence Variants

NIAID Data Ecosystem2026-03-11 收录

转录因子预测

基因序列变异

数据链接：

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE130837 数据链接链接失效反馈

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资源简介：

This SuperSeries is composed of the SubSeries listed below. Refer to individual Series

应用场景：

创建时间：

2019-08-07

序列基序分析

转录因子预测

Rank of the “correct” motif in the output of various programs. “Target” refers to data sets defined in [3]. “Ref.” gives the accession number in the JASPAR or miRbase database, or the target consensus

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QBiC-Pred: Quantitative Predictions of Transcription Factor Binding Changes Due to Sequence Variants V. QBiC-Pred: Quantitative Predictions of Transcription Factor Binding Changes Due to Sequence Variants V

转录因子预测

基因变异功能效应

Non-coding genetic variants/mutations can play functional roles in the cell by disrupting regulatory interactions between transcription factors (TFs) and their genomic target sites. For most human TFs

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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

Classification results on the H1-hESC test data set.

基序发现

转录因子预测

We display the sensitivity (for specificity of 99%) on the independent test data set . Here we pick the optimal model from the cross validation experiment (green) and compare it with the PWM model (bl

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The Effect of Orthology and Coregulation on Detecting Regulatory Motifs

转录因子预测

调控基序检测

BackgroundComputational de novo discovery of transcription factor binding sites is still a challenging problem. The growing number of sequenced genomes allows integrating orthology evidence with coreg

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QBiC-Pred: Quantitative Predictions of Transcription Factor Binding Changes Due to Sequence Variants

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