AIMC Topic: Forkhead Transcription Factors

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Machine learning-assisted decoding of temporal transcriptional dynamics via fluorescent timer.

Nature communications Jul 1, 2025
Investigating the temporal dynamics of gene expression is crucial for understanding gene regulation across various biological processes. Using the Fluorescent Timer protein, the Timer-of-cell-kinetics-and-activity system enables analysis of transcrip...
Mice Mice, Inbred C57BL CRISPR-Cas Systems Flow Cytometry Machine Learning Animals Single-Cell Analysis Spleen Transcription, Genetic Enhancer Elements, Genetic Genes, Reporter Forkhead Transcription Factors T-Lymphocytes, Regulatory Gene Expression Regulation Neural Networks, Computer
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Integrative analysis with machine learning identifies diagnostic and prognostic signatures in neuroblastoma based on differentially DNA methylated enhancers between INSS stage 4 and 4S neuroblastoma.

Journal of cancer research and clinical oncology Mar 21, 2024
INTRODUCTION: Accumulating evidence demonstrates that aberrant methylation of enhancers is crucial in gene expression profiles across several cancers. However, the latent effect of differently expressed enhancers between INSS stage 4S and 4 neuroblas...
Tumor Microenvironment DNA Forkhead Transcription Factors Machine Learning Humans Calcium-Binding Proteins Prognosis Neuroblastoma Risk Factors Trans-Activators
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Integrative analysis of potential diagnostic markers and therapeutic targets for glomerulus-associated diabetic nephropathy based on cellular senescence.

Frontiers in immunology Jan 8, 2024
INTRODUCTION: Diabetic nephropathy (DN), distinguished by detrimental changes in the renal glomeruli, is regarded as the leading cause of death from end-stage renal disease among diabetics. Cellular senescence plays a paramount role, profoundly affec...
Renal Insufficiency, Chronic Cellular Senescence Diabetes Mellitus Molecular Docking Simulation Humans Forkhead Transcription Factors Mice Kidney Glomerulus Aged Diabetic Nephropathies Animals
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Assessment of associations between clinical and immune microenvironmental factors and tumor mutation burden in resected nonsmall cell lung cancer by applying machine learning to whole-slide images.

Cancer medicine May 12, 2020
BACKGROUND: It is unclear whether clinical factors and immune microenvironment (IME) factors are associated with tumor mutation burden (TMB) in patients with nonsmall cell lung cancer (NSCLC).
Antigens, Neoplasm Adenocarcinoma Carcinoma, Non-Small-Cell Lung Lymphocytes, Tumor-Infiltrating Middle Aged Lung Neoplasms B7-H1 Antigen Carcinoembryonic Antigen Smokers Exome Sequencing Keratin-19 Regression Analysis Mutation Forkhead Transcription Factors Female Carcinoma, Squamous Cell Aged Humans Tumor Microenvironment Machine Learning Male Aged, 80 and over Ex-Smokers Adult Non-Smokers
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Assessment of Functional Phosphatidylinositol 3-Kinase Pathway Activity in Cancer Tissue Using Forkhead Box-O Target Gene Expression in a Knowledge-Based Computational Model.

The American journal of pathology Jul 6, 2018
The phosphatidylinositol 3-kinase (PI3K) pathway is commonly activated in cancer. Tumors are potentially sensitive to PI3K pathway inhibitors, but reliable diagnostic tests that assess functional PI3K activity are lacking. Because PI3K pathway activi...
Diagnostic Tests, Routine Phosphatidylinositol 3-Kinases Forkhead Transcription Factors Knowledge Bases Female Breast Neoplasms Gene Expression Profiling Cell Proliferation Phosphorylation Humans Computational Biology Superoxide Dismutase
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Comparative analysis of weighted gene co-expression networks in human and mouse.

PloS one Nov 21, 2017
The application of complex network modeling to analyze large co-expression data sets has gained traction during the last decade. In particular, the use of the weighted gene co-expression network analysis framework has allowed an unbiased and systems-...
Genetic Association Studies Core Binding Factor Alpha 1 Subunit Forkhead Transcription Factors Gene Expression Profiling Gene Regulatory Networks Homeodomain Proteins Humans Mice Gene Expression Regulation Sequence Homology Retinoic Acid Receptor alpha Animals Tissue Distribution Gene Ontology
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NNAlign: a platform to construct and evaluate artificial neural network models of receptor-ligand interactions.

Nucleic acids research Jul 3, 2017
Peptides are extensively used to characterize functional or (linear) structural aspects of receptor-ligand interactions in biological systems, e.g. SH2, SH3, PDZ peptide-recognition domains, the MHC membrane receptors and enzymes such as kinases and ...
Basic Helix-Loop-Helix Leucine Zipper Transcription Factors Humans Internet Forkhead Transcription Factors Peptides Cell Cycle Proteins Neural Networks, Computer HLA-B7 Antigen Protein Binding Amino Acid Sequence Binding Sites Software HLA-B8 Antigen Trans-Activators Databases, Protein HLA-DRB1 Chains Algorithms Ligands Sequence Alignment HLA-A1 Antigen
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