AIMC Topic: Transcription Factors

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Deep learning reveals determinants of transcriptional infidelity at nucleotide resolution in the allopolyploid line by goldfish and common carp hybrids.

Briefings in bioinformatics May 1, 2025
During DNA transcription, the central dogma states that DNA generates corresponding RNA sequences based on the principle of complementary base pairing. However, in the allopolyploid line by goldfish and common carp hybrids, there is a significant lev...
Goldfish Polyploidy Animals Deep Learning Transcription Factors Carps Transcription, Genetic
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Identifying the DNA methylation preference of transcription factors using ProtBERT and SVM.

PLoS computational biology May 1, 2025
Transcription factors (TFs) can affect gene expression by binding to certain specific DNA sequences. This binding process of TFs may be modulated by DNA methylation. A subset of TFs that serve as methylation readers preferentially binds to certain me...
Humans Algorithms Animals Support Vector Machine DNA Methylation DNA Mice Computational Biology Transcription Factors
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Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data.

Nucleic acids research Feb 27, 2025
Gene regulatory networks (GRNs) provide a global representation of how genetic/genomic information is transferred in living systems and are a key component in understanding genome regulation. Single-cell multiome data provide unprecedented opportunit...
Animals Deep Learning Single-Cell Analysis Humans Core Binding Factor Alpha 2 Subunit Genomics Gene Regulatory Networks Transcription Factors Mice Alzheimer Disease
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miRStart 2.0: enhancing miRNA regulatory insights through deep learning-based TSS identification.

Nucleic acids research Jan 6, 2025
MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression by binding to the 3'-untranslated regions of target mRNAs, influencing various biological processes at the post-transcriptional level. Identifying miRNA transcription start si...
Mice Transcription Factors MicroRNAs Animals Deep Learning Gene Expression Regulation Transcription Initiation Site Humans Software
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A self-attention-driven deep learning framework for inference of transcriptional gene regulatory networks.

Briefings in bioinformatics Nov 22, 2024
The interactions between transcription factors (TFs) and the target genes could provide a basis for constructing gene regulatory networks (GRNs) for mechanistic understanding of various biological complex processes. From gene expression data, particu...
Deep Learning Gene Regulatory Networks Computational Biology Transcription Factors Gene Expression Regulation Algorithms Gene Expression Profiling Transcriptome Humans Software
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FunlncModel: integrating multi-omic features from upstream and downstream regulatory networks into a machine learning framework to identify functional lncRNAs.

Briefings in bioinformatics Nov 22, 2024
Accumulating evidence indicates that long noncoding RNAs (lncRNAs) play important roles in molecular and cellular biology. Although many algorithms have been developed to reveal their associations with complex diseases by using downstream targets, th...
Multiomics Transcription Factors RNA, Long Noncoding Algorithms Gene Regulatory Networks Computational Biology Machine Learning Software Humans
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Predicting bacterial transcription factor binding sites through machine learning and structural characterization based on DNA duplex stability.

Briefings in bioinformatics Sep 23, 2024
Transcriptional factors (TFs) in bacteria play a crucial role in gene regulation by binding to specific DNA sequences, thereby assisting in the activation or repression of genes. Despite their central role, deciphering shape recognition of bacterial ...
Binding Sites DNA, Bacterial Protein Binding Transcription Factors DNA Bacterial Proteins Nucleic Acid Conformation Machine Learning Algorithms Bacteria Computational Biology
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MultiSC: a deep learning pipeline for analyzing multiomics single-cell data.

Briefings in bioinformatics Sep 23, 2024
Single-cell technologies enable researchers to investigate cell functions at an individual cell level and study cellular processes with higher resolution. Several multi-omics single-cell sequencing techniques have been developed to explore various as...
Deep Learning Single-Cell Analysis Gene Regulatory Networks Computational Biology Humans Multiomics Transcription Factors
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MLSNet: a deep learning model for predicting transcription factor binding sites.

Briefings in bioinformatics Sep 23, 2024
Accurate prediction of transcription factor binding sites (TFBSs) is essential for understanding gene regulation mechanisms and the etiology of diseases. Despite numerous advances in deep learning for predicting TFBSs, their performance can still be ...
DNA Humans Binding Sites Algorithms Computational Biology Chromatin Immunoprecipitation Sequencing Deep Learning Transcription Factors
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An integrated machine-learning model to predict nucleosome architecture.

Nucleic acids research Sep 23, 2024
We demonstrate that nucleosomes placed in the gene body can be accurately located from signal decay theory assuming two emitters located at the beginning and at the end of genes. These generated wave signals can be in phase (leading to well defined n...
Humans Machine Learning Transcription Factors DNA Binding Sites Nucleosomes
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