AIMC Topic: Transcription Factors

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Predicting splicing patterns from the transcription factor binding sites in the promoter with deep learning.

BMC genomics Sep 3, 2024
BACKGROUND: Alternative splicing is a pivotal mechanism of post-transcriptional modification that contributes to the transcriptome plasticity and proteome diversity in metazoan cells. Although many splicing regulations around the exon/intron regions ...
Humans Deep Learning Binding Sites Exons Alternative Splicing Transcription Factors Promoter Regions, Genetic Computational Biology
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Cross-Species Prediction of Transcription Factor Binding by Adversarial Training of a Novel Nucleotide-Level Deep Neural Network.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Jul 30, 2024
Cross-species prediction of TF binding remains a major challenge due to the rapid evolutionary turnover of individual TF binding sites, resulting in cross-species predictive performance being consistently worse than within-species performance. In thi...
Animals Deep Learning Mice Humans Neural Networks, Computer Binding Sites Polymorphism, Single Nucleotide Transcription Factors Computational Biology Nucleotides Species Specificity
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AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors.

Molecules (Basel, Switzerland) Jul 26, 2024
The rational design, activity prediction, and adaptive application of biological elements (bio-elements) are crucial research fields in synthetic biology. Currently, a major challenge in the field is efficiently designing desired bio-elements and acc...
Deep Learning Biosensing Techniques Transcription Factors Machine Learning Algorithms Synthetic Biology Humans Artificial Intelligence
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TFscope: systematic analysis of the sequence features involved in the binding preferences of transcription factors.

Genome biology Jul 10, 2024
Characterizing the binding preferences of transcription factors (TFs) in different cell types and conditions is key to understand how they orchestrate gene expression. Here, we develop TFscope, a machine learning approach that identifies sequence fea...
Chromatin Immunoprecipitation Sequencing Binding Sites Nucleotide Motifs Humans Protein Binding Machine Learning Transcription Factors
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Nonlinear classifiers for wet-neuromorphic computing using gene regulatory neural network.

Biophysical reports Jun 5, 2024
The gene regulatory network (GRN) of biological cells governs a number of key functionalities that enable them to adapt and survive through different environmental conditions. Close observation of the GRN shows that the structure and operational prin...
Transcription Factors Escherichia coli Nonlinear Dynamics Neural Networks, Computer Machine Learning Gene Regulatory Networks
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PPRTGI: A Personalized PageRank Graph Neural Network for TF-Target Gene Interaction Detection.

IEEE/ACM transactions on computational biology and bioinformatics Jun 5, 2024
Transcription factors (TFs) regulation is required for the vast majority of biological processes in living organisms. Some diseases may be caused by improper transcriptional regulation. Identifying the target genes of TFs is thus critical for underst...
Databases, Genetic Sequence Analysis, DNA Algorithms Animals Neural Networks, Computer Computational Biology Humans Transcription Factors Gene Regulatory Networks
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Identification of drug responsive enhancers by predicting chromatin accessibility change from perturbed gene expression profiles.

NPJ systems biology and applications May 30, 2024
Individual may response to drug treatment differently due to their genetic variants located in enhancers. These variants can alter transcription factor's (TF) binding strength, affect enhancer's chromatin activity or interaction, and eventually chang...
Genome-Wide Association Study Enhancer Elements, Genetic Humans Transcriptome Computational Biology Chromatin Gene Expression Profiling Transcription Factors Pharmacogenetics Machine Learning
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Prediction of Transcription Factor Binding Sites on Cell-Free DNA Based on Deep Learning.

Journal of chemical information and modeling May 9, 2024
Transcription factors (TFs) are important regulatory elements for vital cellular activities, and the identification of transcription factor binding sites (TFBS) can help to explore gene regulatory mechanisms. Research studies have proved that cfDNA (...
Transcription Factors Humans Cell-Free Nucleic Acids Binding Sites DNA Deep Learning
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Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights.

BioEssays : news and reviews in molecular, cellular and developmental biology May 8, 2024
Understanding the influence of cis-regulatory elements on gene regulation poses numerous challenges given complexities stemming from variations in transcription factor (TF) binding, chromatin accessibility, structural constraints, and cell-type diffe...
Humans Machine Learning Gene Regulatory Networks Computational Biology Animals Gene Expression Regulation Transcription Factors CRISPR-Cas Systems Regulatory Elements, Transcriptional
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Predicting Transcription Factor Binding Sites with Deep Learning.

International journal of molecular sciences May 3, 2024
Prediction of binding sites for transcription factors is important to understand how the latter regulate gene expression and how this regulation can be modulated for therapeutic purposes. A consistent number of references address this issue with diff...
HeLa Cells Binding Sites Chromatin Immunoprecipitation Sequencing Transcription Factors Cell Line Deep Learning Humans Protein Binding Computational Biology
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