AIMC Topic: Mice
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Artificial intelligence guided discovery of a barrier-protective therapy in inflammatory bowel disease.
Nature communications
Jul 12, 2021
Modeling human diseases as networks simplify complex multi-cellular processes, helps understand patterns in noisy data that humans cannot find, and thereby improves precision in prediction. Using Inflammatory Bowel Disease (IBD) as an example, here w...
Dextran Sulfate
Colitis
Organoids
Multigene Family
Gene Expression Regulation
Molecular Targeted Therapy
Likelihood Functions
Cohort Studies
Mice
Treatment Outcome
Disease Models, Animal
Mice, Inbred C57BL
Intestinal Mucosa
Mice, Knockout
AMP-Activated Protein Kinases
Machine Learning
Inflammatory Bowel Diseases
Reproducibility of Results
Animals
Humans
Artificial Intelligence
An inflammatory aging clock (iAge) based on deep learning tracks multimorbidity, immunosenescence, frailty and cardiovascular aging.
Nature aging
Jul 12, 2021
While many diseases of aging have been linked to the immunological system, immune metrics capable of identifying the most at-risk individuals are lacking. From the blood immunome of 1,001 individuals aged 8-96 years, we developed a deep-learning meth...
Partial information decomposition reveals that synergistic neural integration is greater downstream of recurrent information flow in organotypic cortical cultures.
PLoS computational biology
Jul 12, 2021
The directionality of network information flow dictates how networks process information. A central component of information processing in both biological and artificial neural networks is their ability to perform synergistic integration-a type of co...
Structure-aware machine learning identifies microRNAs operating as Toll-like receptor 7/8 ligands.
RNA biology
Jul 9, 2021
MicroRNAs (miRNAs) can serve as activation signals for membrane receptors, a recently discovered function that is independent of the miRNAs' conventional role in post-transcriptional gene regulation. Here, we introduce a machine learning approach, Br...
On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: Chronicles of the MEMENTO challenge.
NeuroImage
Jul 6, 2021
Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the las...
Optical tissue clearing and machine learning can precisely characterize extravasation and blood vessel architecture in brain tumors.
Communications biology
Jul 1, 2021
Precise methods for quantifying drug accumulation in brain tissue are currently very limited, challenging the development of new therapeutics for brain disorders. Transcardial perfusion is instrumental for removing the intravascular fraction of an in...
A Deep Learning Approach for Segmentation, Classification, and Visualization of 3-D High-Frequency Ultrasound Images of Mouse Embryos.
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Jun 29, 2021
Segmentation and mutant classification of high-frequency ultrasound (HFU) mouse embryo brain ventricle (BV) and body images can provide valuable information for developmental biologists. However, manual segmentation and identification of BV and body ...
Automated Detection and Diameter Estimation for Mouse Mesenteric Artery Using Semantic Segmentation.
Journal of vascular research
Jun 28, 2021
BACKGROUND: Pressurized myography is useful for the assessment of small artery structures and function. However, this procedure requires technical expertise for sample preparation and effort to choose an appropriate sized artery. In this study, we de...
scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics.
Nature communications
Jun 22, 2021
Single-cell omics is the fastest-growing type of genomics data in the literature and public genomics repositories. Leveraging the growing repository of labeled datasets and transferring labels from existing datasets to newly generated datasets will e...
Microbiome Preprocessing Machine Learning Pipeline.
Frontiers in immunology
Jun 18, 2021
BACKGROUND: 16S sequencing results are often used for Machine Learning (ML) tasks. 16S gene sequences are represented as feature counts, which are associated with taxonomic representation. Raw feature counts may not be the optimal representation for ...
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