AIMC Topic: Trans-Activators

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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...
Risk Factors Tumor Microenvironment Machine Learning Prognosis Neuroblastoma Trans-Activators Calcium-Binding Proteins DNA Forkhead Transcription Factors Humans
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Carboxybetaine-modified succinylated chitosan-based beads encourage pancreatic β-cells (Min-6) to form islet-like spheroids under in vitro conditions.

Journal of materials science. Materials in medicine Dec 30, 2017
In vitro, pancreatic β-cells tend to reduce their ability to aggregate into islets and lose insulin-producing ability, likely due to insufficient cell-cell and cell-matrix interactions that are essential for β-cell retention, viability and functional...
Insulin Secretion Lasers Trans-Activators Cadherins Scattering, Radiation Molecular Dynamics Simulation Spheroids, Cellular Betaine Carbon Homeodomain Proteins Insulin-Secreting Cells Mice Chitosan Microscopy, Electron, Scanning Animals Cell Survival Spectroscopy, Fourier Transform Infrared Cell Culture Techniques Microscopy, Fluorescence Surface Properties Insulin
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Prospective identification of hematopoietic lineage choice by deep learning.

Nature methods Feb 20, 2017
Differentiation alters molecular properties of stem and progenitor cells, leading to changes in their shape and movement characteristics. We present a deep neural network that prospectively predicts lineage choice in differentiating primary hematopoi...
Time-Lapse Imaging Gene Knock-In Techniques Mice, Mutant Strains Cell Differentiation Neural Networks, Computer Trans-Activators Image Processing, Computer-Assisted Machine Learning Proto-Oncogene Proteins Animals Biomarkers Area Under Curve Hematopoietic Stem Cells Male Cell Lineage
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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 Internet Protein Binding Databases, Protein Ligands Cell Cycle Proteins Forkhead Transcription Factors Amino Acid Sequence Neural Networks, Computer Software Algorithms Peptides Humans HLA-B7 Antigen HLA-B8 Antigen HLA-DRB1 Chains Sequence Alignment HLA-A1 Antigen Binding Sites Trans-Activators
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