AIMC Topic: Islets of Langerhans
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Predicting type 2 diabetes via machine learning integration of multiple omics from human pancreatic islets.
Scientific reports
Jun 25, 2024
Type 2 diabetes (T2D) is the fastest growing non-infectious disease worldwide. Impaired insulin secretion from pancreatic beta-cells is a hallmark of T2D, but the mechanisms behind this defect are insufficiently characterized. Integrating multiple la...
Machine-learning-guided recognition of α and β cells from label-free infrared micrographs of living human islets of Langerhans.
Scientific reports
Jun 20, 2024
Human islets of Langerhans are composed mostly of glucagon-secreting α cells and insulin-secreting β cells closely intermingled one another. Current methods for identifying α and β cells involve either fixing islets and using immunostaining or disagg...
Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets.
Cell reports. Medicine
Apr 26, 2024
Type 1 diabetes (T1D) is a chronic condition in which beta cells are destroyed by immune cells. Despite progress in immunotherapies that could delay T1D onset, early detection of autoimmunity remains challenging. Here, we evaluate the utility of mach...
Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits.
Proceedings of the National Academy of Sciences of the United States of America
Aug 21, 2023
Genetic association studies have identified hundreds of independent signals associated with type 2 diabetes (T2D) and related traits. Despite these successes, the identification of specific causal variants underlying a genetic association signal rema...
CoRE-ATAC: A deep learning model for the functional classification of regulatory elements from single cell and bulk ATAC-seq data.
PLoS computational biology
Dec 13, 2021
Cis-Regulatory elements (cis-REs) include promoters, enhancers, and insulators that regulate gene expression programs via binding of transcription factors. ATAC-seq technology effectively identifies active cis-REs in a given cell type (including from...
Manipulating cellular microRNAs and analyzing high-dimensional gene expression data using machine learning workflows.
STAR protocols
Oct 23, 2021
MicroRNAs (miRNAs) are elements of the gene regulatory network and manipulating their abundance is essential toward elucidating their role in patho-physiological conditions. We present a detailed workflow that identifies important miRNAs using a mach...
Discriminating the single-cell gene regulatory networks of human pancreatic islets: A novel deep learning application.
Computers in biology and medicine
Feb 6, 2021
Analysis of single-cell pancreatic data can play an important role in understanding various metabolic diseases and health conditions. Due to the sparsity and noise present in such single-cell gene expression data, inference of single-cell gene regula...
Artificial Intelligence Analysis of Magnetic Particle Imaging for Islet Transplantation in a Mouse Model.
Molecular imaging and biology
Aug 24, 2020
PURPOSE: Current approaches to quantification of magnetic particle imaging (MPI) for cell-based therapy are thwarted by the lack of reliable, standardized methods of segmenting the signal from background in images. This calls for the development of a...
Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis.
Nature communications
May 11, 2020
Single-cell RNA sequencing (scRNA-seq) can characterize cell types and states through unsupervised clustering, but the ever increasing number of cells and batch effect impose computational challenges. We present DESC, an unsupervised deep embedding a...
Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals.
eLife
Jan 27, 2020
Genome-wide association analyses have uncovered multiple genomic regions associated with T2D, but identification of the causal variants at these remains a challenge. There is growing interest in the potential of deep learning models - which predict e...
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