AIMC Topic: Transcription Factors

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MAResNet: predicting transcription factor binding sites by combining multi-scale bottom-up and top-down attention and residual network.

Briefings in bioinformatics Jan 17, 2022
Accurate identification of transcription factor binding sites is of great significance in understanding gene expression, biological development and drug design. Although a variety of methods based on deep-learning models and large-scale data have bee...
Binding Sites Neural Networks, Computer Deep Learning Protein Binding Transcription Factors
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Assessing deep learning methods in cis-regulatory motif finding based on genomic sequencing data.

Briefings in bioinformatics Jan 17, 2022
Identifying cis-regulatory motifs from genomic sequencing data (e.g. ChIP-seq and CLIP-seq) is crucial in identifying transcription factor (TF) binding sites and inferring gene regulatory mechanisms for any organism. Since 2015, deep learning (DL) me...
Transcription Factors Base Sequence Binding Sites Algorithms Deep Learning Chromatin Immunoprecipitation
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DeepMotifSyn: a deep learning approach to synthesize heterodimeric DNA motifs.

Briefings in bioinformatics Jan 17, 2022
The cooperativity of transcription factors (TFs) is a widespread phenomenon in the gene regulation system. However, the interaction patterns between TF binding motifs remain elusive. The recent high-throughput assays, CAP-SELEX, have identified over ...
Protein Binding Transcription Factors Humans Binding Sites Deep Learning Nucleotide Motifs
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Genome-wide association study-based prediction of atrial fibrillation using artificial intelligence.

Open heart Jan 1, 2022
OBJECTIVE: We previously reported early-onset atrial fibrillation (AF) associated genetic loci among a Korean population. We explored whether the AF-associated single-nucleotide polymorphisms (SNPs) selected from the Genome-Wide Association Study (GW...
Female Male Middle Aged Genome-Wide Association Study Humans Artificial Intelligence Republic of Korea Homeodomain Proteins Homeobox Protein PITX2 DNA Morbidity Atrial Fibrillation Genetic Predisposition to Disease Polymorphism, Single Nucleotide Transcription Factors
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Epitome: predicting epigenetic events in novel cell types with multi-cell deep ensemble learning.

Nucleic acids research Nov 8, 2021
The accumulation of large epigenomics data consortiums provides us with the opportunity to extrapolate existing knowledge to new cell types and conditions. We propose Epitome, a deep neural network that learns similarities of chromatin accessibility ...
Binding Sites Cell Lineage Atlases as Topic Chromatin Chromatin Immunoprecipitation Eukaryotic Cells Cell Communication Genome, Human Histones Protein Binding Epigenesis, Genetic Transcription Factors Machine Learning Software Humans Neural Networks, Computer
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High-resolution transcription factor binding sites prediction improved performance and interpretability by deep learning method.

Briefings in bioinformatics Nov 5, 2021
Transcription factors (TFs) are essential proteins in regulating the spatiotemporal expression of genes. It is crucial to infer the potential transcription factor binding sites (TFBSs) with high resolution to promote biology and realize precision med...
Transcription Factors Binding Sites Chromatin Immunoprecipitation Deep Learning Protein Binding Computational Biology
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Explainability in transformer models for functional genomics.

Briefings in bioinformatics Sep 2, 2021
The effectiveness of deep learning methods can be largely attributed to the automated extraction of relevant features from raw data. In the field of functional genomics, this generally concerns the automatic selection of relevant nucleotide motifs fr...
Base Sequence Promoter Regions, Genetic Binding Sites Deep Learning Genome, Bacterial DNA, Bacterial Genomics Transcription Initiation Site Transcription Factors Escherichia coli
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iDHS-Deep: an integrated tool for predicting DNase I hypersensitive sites by deep neural network.

Briefings in bioinformatics Sep 2, 2021
DNase I hypersensitive site (DHS) refers to the hypersensitive region of chromatin for the DNase I enzyme. It is an important part of the noncoding region and contains a variety of regulatory elements, such as promoter, enhancer, and transcription fa...
Transcription, Genetic Enhancer Elements, Genetic Arabidopsis Chromatin Deoxyribonuclease I Genetic Loci Datasets as Topic Software Promoter Regions, Genetic Deep Learning DNA Internet Protein Binding Oryza Transcription Factors Humans
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Locating transcription factor binding sites by fully convolutional neural network.

Briefings in bioinformatics Sep 2, 2021
Transcription factors (TFs) play an important role in regulating gene expression, thus identification of the regions bound by them has become a fundamental step for molecular and cellular biology. In recent years, an increasing number of deep learnin...
Sequence Analysis, Protein Response Elements Chromatin Immunoprecipitation Sequencing MCF-7 Cells Neural Networks, Computer Humans Transcription Factors A549 Cells Sequence Analysis, DNA Amino Acid Motifs
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A self-attention model for inferring cooperativity between regulatory features.

Nucleic acids research Jul 21, 2021
Deep learning has demonstrated its predictive power in modeling complex biological phenomena such as gene expression. The value of these models hinges not only on their accuracy, but also on the ability to extract biologically relevant information fr...
Nucleotide Motifs Regulatory Elements, Transcriptional Arabidopsis Chromatin Immunoprecipitation Sequencing Transcription Factors Humans Cell Line Deep Learning Promoter Regions, Genetic Genomics
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