AIMC Topic: Transcription Factors

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FCNGRU: Locating Transcription Factor Binding Sites by Combing Fully Convolutional Neural Network With Gated Recurrent Unit.

IEEE journal of biomedical and health informatics Apr 14, 2022
Deciphering the relationship between transcription factors (TFs) and DNA sequences is very helpful for computational inference of gene regulation and a comprehensive understanding of gene regulation mechanisms. Transcription factor binding sites (TFB...
Neural Networks, Computer Protein Binding Humans DNA Computational Biology Transcription Factors Nucleotides Binding Sites
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Chromatin interaction-aware gene regulatory modeling with graph attention networks.

Genome research Apr 8, 2022
Linking distal enhancers to genes and modeling their impact on target gene expression are longstanding unresolved problems in regulatory genomics and critical for interpreting noncoding genetic variation. Here, we present a new deep learning approach...
Chromatin Neural Networks, Computer Binding Sites Transcription Factors Gene Regulatory Networks Regulatory Sequences, Nucleic Acid Gene Expression Regulation
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DeepCAGE: Incorporating Transcription Factors in Genome-wide Prediction of Chromatin Accessibility.

Genomics, proteomics & bioinformatics Mar 12, 2022
Although computational approaches have been complementing high-throughput biological experiments for the identification of functional regions in the human genome, it remains a great challenge to systematically decipher interactions between transcript...
Binding Sites Regulatory Sequences, Nucleic Acid Transcription Factors Genome, Human Chromatin Chromatin Assembly and Disassembly Humans Deep Learning Protein Binding
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Base-resolution prediction of transcription factor binding signals by a deep learning framework.

PLoS computational biology Mar 9, 2022
Transcription factors (TFs) play an important role in regulating gene expression, thus the identification of the sites bound by them has become a fundamental step for molecular and cellular biology. In this paper, we developed a deep learning framewo...
Deep Learning Protein Binding Transcription Factors Binding Sites Chromatin Immunoprecipitation Sequencing
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Domain-adaptive neural networks improve cross-species prediction of transcription factor binding.

Genome research Jan 18, 2022
The intrinsic DNA sequence preferences and cell type-specific cooperative partners of transcription factors (TFs) are typically highly conserved. Hence, despite the rapid evolutionary turnover of individual TF binding sites, predictive sequence model...
Neural Networks, Computer Chromatin Immunoprecipitation Sequencing Transcription Factors Binding Sites Protein Binding Computational Biology
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A GO catalogue of human DNA-binding transcription factors.

Biochimica et biophysica acta. Gene regulatory mechanisms Oct 18, 2021
To control gene transcription, DNA-binding transcription factors recognise specific sequence motifs in gene regulatory regions. A complete and reliable GO annotation of all DNA-binding transcription factors is key to investigating the delicate balanc...
Databases, Genetic DNA Gene Ontology Molecular Sequence Annotation Humans Transcription Factors
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Multi-Scale Capsule Network for Predicting DNA-Protein Binding Sites.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2021
Discovering DNA-protein binding sites, also known as motif discovery, is the foundation for further analysis of transcription factors (TFs). Deep learning algorithms such as convolutional neural networks (CNN) have been introduced to motif discovery ...
Sequence Analysis, Protein Algorithms Chromatin Immunoprecipitation Sequencing Deep Learning Protein Binding Transcription Factors DNA-Binding Proteins Computational Biology Binding Sites
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Predicting TF-DNA Binding Motifs from ChIP-seq Datasets Using the Bag-Based Classifier Combined With a Multi-Fold Learning Scheme.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2021
The rapid development of high-throughput sequencing technology provides unique opportunities for studying of transcription factor binding sites, but also brings new computational challenges. Recently, a series of discriminative motif discovery (DMD) ...
Algorithms Computational Biology Transcription Factors DNA Humans Machine Learning Binding Sites Chromatin Immunoprecipitation Sequencing
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Biologically relevant transfer learning improves transcription factor binding prediction.

Genome biology Sep 27, 2021
BACKGROUND: Deep learning has proven to be a powerful technique for transcription factor (TF) binding prediction but requires large training datasets. Transfer learning can reduce the amount of data required for deep learning, while improving overall...
Genome Machine Learning Chromatin Immunoprecipitation Sequencing Transcription Factors
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Evaluation of deep learning approaches for modeling transcription factor sequence specificity.

Genomics Sep 14, 2021
As a key component of gene regulation, transcription factors (TFs) play an important role in a number of biological processes. To fully understand the underlying mechanism of TF-mediated gene regulation, it is therefore critical to accurately identif...
DNA Binding Sites Humans Deep Learning Neural Networks, Computer Transcription Factors
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