Using Deep Learning to Predict Transcription Factor Binding Sites Based on Multiple-omics DataTranscription factors (TFs) have a great effect on gene transcription process. TFs can boost the formation of complex gene expression regulation system by promoting or inhibiting gene binding to DNA, which ...
FactorNet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data Due to the large numbers of transcription factors (TFs) and cell types, querying binding profiles of all TF/cell type pairs is not experimentally feasible, owin...
A universal deep-learning model for zinc finger design enables transcription factor reprogramming Abstract Cys2His2zinc finger (ZF) domains engineered to bind specific target sequences in the genome provide an effective strategy for programmable regulation of gene expression, with many potential therapeuti...
在这里,我们报告了基于深度学习的工具DeepTFactor的开发,该工具可以预测所讨论的蛋白质是否为TF。DeepTFactor使用卷积神经网络来提取蛋白质的特征。它在预测真核和原核来源的TF方面显示出高性能,从而导致F1分分别为0.8154和0.8000。关于输入的预测分数梯度的分析表明,DeepTFactor检测到TF预测的DNA结合域和其他潜在特征。
We present Transcription factors cooperativity Inference Analysis with Neural Attention (TIANA), a deep learning framework that focuses on interpretability. In this study, we demonstrated that TIANA could discover biologically relevant insights into co-occurring pairs of transcription factor motifs. Compared...
Generating specificity in genome regulation through transcription factor sensitivity to chromatin Article 12 July 2022 Position-dependent function of human sequence-specific transcription factors Article Open access 17 July 2024 Nuclear compartmentalization as a mechanism of quantitative control of gene ex...
Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. Nat Biotechnol. 2015;33(8):831. Article CAS PubMed Google Scholar Qin Q, Feng J. Imputation for transcription factor binding predictions based on deep learning. PLoS Comput Biol. 2017;13(2):1005403. ...
I will describe the principles and mechanisms involved in the combinatorial requirement of transcription factor binding motifs for enhancer activity, including the role of chromatin accessibility, repressors, and low-affinity motifs in the cis-regulatory code. Deciphering the cis-regulatory code would ...
Recently, deep learning based models have been proposed and have shown competitive results on a transcription factor binding site prediction task. However, it is difficult to interpret the prediction results obtained from the previous models. In addition, the previous models assumed all the sequence ...
A universal deep-learning model for zinc finger design enables transcription factor reprogramming Nat Biotechnol, 41 (2023), pp. 1117-1129 CrossrefView in ScopusGoogle Scholar [98] Q. Zhou, J. Brown, A. Kanarek, J. Rajagopal D. a Melton, in vivo reprogramming of adult pancreatic exocrine ...