AIMC Topic: Gene Expression Regulation

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From data to discovery: AI-guided analysis of disease-relevant molecules in spinal muscular atrophy (SMA).

Human molecular genetics Jul 22, 2024
Spinal Muscular Atrophy is caused by partial loss of survival of motoneuron (SMN) protein expression. The numerous interaction partners and mechanisms influenced by SMN loss result in a complex disease. Current treatments restore SMN protein levels t...
Humans Artificial Intelligence Machine Learning Algorithms Disease Models, Animal Gene Expression Regulation Muscular Atrophy, Spinal Survival of Motor Neuron 1 Protein Animals Mice Computational Biology
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Enhancer-MDLF: a novel deep learning framework for identifying cell-specific enhancers.

Briefings in bioinformatics Jan 22, 2024
Enhancers, noncoding DNA fragments, play a pivotal role in gene regulation, facilitating gene transcription. Identifying enhancers is crucial for understanding genomic regulatory mechanisms, pinpointing key elements and investigating networks governi...
Gene Expression Regulation Deep Learning Genomics Humans Promoter Regions, Genetic Enhancer Elements, Genetic
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Design and deep learning of synthetic B-cell-specific promoters.

Nucleic acids research Nov 27, 2023
Synthetic biology and deep learning synergistically revolutionize our ability for decoding and recoding DNA regulatory grammar. The B-cell-specific transcriptional regulation is intricate, and unlock the potential of B-cell-specific promoters as synt...
DNA Animals Deep Learning Gene Expression Regulation Mice Humans Promoter Regions, Genetic Immunoglobulin Variable Region
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Single-cell gene regulatory network prediction by explainable AI.

Nucleic acids research Feb 28, 2023
The molecular heterogeneity of cancer cells contributes to the often partial response to targeted therapies and relapse of disease due to the escape of resistant cell populations. While single-cell sequencing has started to improve our understanding ...
Neoplasms Deep Learning Gene Regulatory Networks Single-Cell Gene Expression Analysis Humans Lung Neoplasms Gene Expression Regulation
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Detection of transcription factors binding to methylated DNA by deep recurrent neural network.

Briefings in bioinformatics Jan 17, 2022
Transcription factors (TFs) are proteins specifically involved in gene expression regulation. It is generally accepted in epigenetics that methylated nucleotides could prevent the TFs from binding to DNA fragments. However, recent studies have confir...
Transcription Factors DNA Methylation DNA Algorithms Binding Sites Deep Learning Humans DNA-Binding Proteins Protein Binding Neural Networks, Computer Gene Expression Regulation
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SIGNET: single-cell RNA-seq-based gene regulatory network prediction using multiple-layer perceptron bagging.

Briefings in bioinformatics Jan 17, 2022
High-throughput single-cell RNA-seq data have provided unprecedented opportunities for deciphering the regulatory interactions among genes. However, such interactions are complex and often nonlinear or nonmonotonic, which makes their inference using ...
Humans Sequence Analysis, RNA Transcription Factors Cluster Analysis Gene Expression Regulation Deep Learning RNA-Seq Neural Networks, Computer Single-Cell Analysis Gene Regulatory Networks Algorithms Gene Expression Profiling
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A deep learning model to identify gene expression level using cobinding transcription factor signals.

Briefings in bioinformatics Jan 17, 2022
Gene expression is directly controlled by transcription factors (TFs) in a complex combination manner. It remains a challenging task to systematically infer how the cooperative binding of TFs drives gene activity. Here, we quantitatively analyzed the...
Gene Expression Gene Expression Regulation Gene Regulatory Networks Deep Learning Transcription Factors
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scEnhancer: a single-cell enhancer resource with annotation across hundreds of tissue/cell types in three species.

Nucleic acids research Jan 7, 2022
Previous studies on enhancers and their target genes were largely based on bulk samples that represent 'average' regulatory activities from a large population of millions of cells, masking the heterogeneity and important effects from the sub-populati...
Molecular Sequence Annotation Cell Lineage Enhancer Elements, Genetic Chromatin Promoter Regions, Genetic Organ Specificity Drosophila melanogaster Consensus Sequence Eukaryotic Cells Gene Regulatory Networks Mice Databases, Genetic Genetic Heterogeneity Gene Expression Regulation Unsupervised Machine Learning Internet Software Animals Single-Cell Analysis Humans
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WEVar: a novel statistical learning framework for predicting noncoding regulatory variants.

Briefings in bioinformatics Nov 5, 2021
Understanding the functional consequence of noncoding variants is of great interest. Though genome-wide association studies or quantitative trait locus analyses have identified variants associated with traits or molecular phenotypes, most of them are...
Regulatory Sequences, Ribonucleic Acid Humans Software Algorithms Models, Statistical Deep Learning Gene Expression Regulation Genetic Variation Computational Biology RNA, Untranslated Databases, Genetic
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Machine learning for phytopathology: from the molecular scale towards the network scale.

Briefings in bioinformatics Sep 2, 2021
With the increasing volume of high-throughput sequencing data from a variety of omics techniques in the field of plant-pathogen interactions, sorting, retrieving, processing and visualizing biological information have become a great challenge. Within...
Pathogen-Associated Molecular Pattern Molecules Plant Pathology Receptors, Pattern Recognition Plant Diseases Gene Expression Regulation Bacterial Proteins Fungal Proteins Protein Interaction Mapping Host-Pathogen Interactions Support Vector Machine NLR Proteins Disease Resistance Viral Proteins Protein Serine-Threonine Kinases Plants Bayes Theorem
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