AIMC Topic: Transcription Factors

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The shared biomarkers and immune landscape in psoriatic arthritis and rheumatoid arthritis: Findings based on bioinformatics, machine learning and single-cell analysis.

PloS one Nov 7, 2024
OBJECTIVE: Psoriatic arthritis (PsA) and rheumatoid arthritis (RA) are the most common types of inflammatory musculoskeletal disorders that share overlapping clinical features and complications. The aim of this study was to identify shared marker gen...
Transcription Factors Humans Gene Regulatory Networks Arthritis, Psoriatic Machine Learning Arthritis, Rheumatoid Biomarkers Computational Biology Gene Expression Profiling Single-Cell Analysis
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Exploration of common pathogenesis and candidate hub genes between HIV and monkeypox co-infection using bioinformatics and machine learning.

Scientific reports Nov 4, 2024
This study explored the pathogenesis of human immunodeficiency virus (HIV) and monkeypox co-infection, identifying candidate hub genes and potential drugs using bioinformatics and machine learning. Datasets for HIV (GSE 37250) and monkeypox (GSE 2412...
Humans Machine Learning Gene Regulatory Networks Computational Biology Transcription Factors Gene Expression Profiling HIV Infections Protein Interaction Maps Mpox, Monkeypox MicroRNAs Gene Ontology Coinfection
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Transcriptional regulation of hypoxic cancer cell metabolism and artificial intelligence.

Trends in cancer Oct 30, 2024
Gene expression regulation in hypoxic tumor microenvironments is mediated by O responsive transcription factors (OR-TFs), fine-tuning cancer cell metabolic demand for O according to its availability. Here, we discuss key OR-TFs and emerging artificia...
Humans Artificial Intelligence Tumor Microenvironment Oxygen Cell Hypoxia Neoplasms Gene Expression Regulation, Neoplastic Transcription Factors Tumor Hypoxia
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Enhancing the Predictive Power of Machine Learning Models through a Chemical Space Complementary DEL Screening Strategy.

Journal of medicinal chemistry Oct 23, 2024
DNA-encoded library (DEL) technology is an effective method for small molecule drug discovery, enabling high-throughput screening against target proteins. While DEL screening produces extensive data, it can reveal complex patterns not easily recogniz...
Small Molecule Libraries Machine Learning E1A-Associated p300 Protein Drug Discovery Bromodomain Containing Proteins Transcription Factors DNA Humans High-Throughput Screening Assays Cell Cycle Proteins
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Identification of mitophagy-related genes and analysis of immune infiltration in the astrocytes based on machine learning in the pathogenesis of major depressive disorder.

Journal of affective disorders Sep 14, 2024
BACKGROUNDS: Major depressive disorder (MDD) is a pervasive mental and mood disorder with complicated and heterogeneous etiology. Mitophagy, a selective autophagy of cells, specifically eliminates dysfunctional mitochondria. The mitochondria dysfunct...
Mitochondria Mitophagy Transcription Factors MicroRNAs Astrocytes Gene Regulatory Networks Computational Biology Humans Machine Learning Depressive Disorder, Major Gene Expression Profiling
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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 ...
Alternative Splicing Promoter Regions, Genetic Binding Sites Exons Humans Deep Learning Computational Biology Transcription Factors
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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...
Mice Polymorphism, Single Nucleotide Computational Biology Transcription Factors Nucleotides Species Specificity Neural Networks, Computer Animals Deep Learning Humans Binding Sites
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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...
Synthetic Biology Humans Deep Learning Artificial Intelligence Biosensing Techniques Machine Learning Transcription Factors Algorithms
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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...
Nucleotide Motifs Protein Binding Transcription Factors Chromatin Immunoprecipitation Sequencing Binding Sites Humans Machine Learning
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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...
Neural Networks, Computer Escherichia coli Machine Learning Nonlinear Dynamics Gene Regulatory Networks Transcription Factors
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