Sequence Features Accurately Predict Genome-wide MeCP2 Binding in vivo

ChEBI2016-03-24 更新2026-07-12 收录

转录因子预测

表观遗传序列分析

数据链接：

https://www.ebi.ac.uk/biostudies/ArrayExpress/studies/E-GEOD-71126 数据链接链接失效反馈

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资源简介：

Sequence Features Accurately Predict Genome-wide MeCP2 Binding in vivo

创建时间：

2016-03-24

TFBindFormer_dataset

转录因子预测

DNA相互作用

This dataset contains the training, validation, and test data used for transcription factor (TF)–DNA binding prediction in the TFBindFormer framework. It integrates genomic DNA sequence bins with tran

Zenodo2026-01-26 更新90

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

NIAID Data Ecosystem40

The 9aaTAD Transactivation Domains: From Gal4 to p53

转录因子预测

转录激活功能验证

The family of the Nine amino acid Transactivation Domain, 9aaTAD family, comprises currently over 40 members. The 9aaTAD domains are universally recognized by the transcriptional machinery from yeast

Figshare2016-09-13 更新20

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

NIAID Data Ecosystem50

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

Sequence Features Accurately Predict Genome-wide MeCP2 Binding in vivo

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