AIMC Topic: Mice

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Detecting spatially co-expressed gene clusters with functional coherence by graph-regularized convolutional neural network.

Bioinformatics (Oxford, England)
MOTIVATION: Clustering spatial-resolved gene expression is an essential analysis to reveal gene activities in the underlying morphological context by their functional roles. However, conventional clustering analysis does not consider gene expression ...

Scan-less machine-learning-enabled incoherent microscopy for minimally-invasive deep-brain imaging.

Optics express
Deep-brain microscopy is strongly limited by the size of the imaging probe, both in terms of achievable resolution and potential trauma due to surgery. Here, we show that a segment of an ultra-thin multi-mode fiber (cannula) can replace the bulky mic...

A neural surveyor to map touch on the body.

Proceedings of the National Academy of Sciences of the United States of America
Perhaps the most recognizable sensory map in all of neuroscience is the somatosensory homunculus. Although it seems straightforward, this simple representation belies the complex link between an activation in a somatotopic map and the associated touc...

Computational Prediction of N- and O-Linked Glycosylation Sites for Human and Mouse Proteins.

Methods in molecular biology (Clifton, N.J.)
Protein glycosylation is one of the most complex posttranslational modifications (PTM) that play a fundamental role in protein function. Identification and annotation of these sites using experimental approaches are challenging and time consuming. He...

[Percellome Project: research on molecular mechanisms of toxicological responses based on transcriptomics and epigenetics].

Nihon yakurigaku zasshi. Folia pharmacologica Japonica
We are constructing the "Percellome Database" containing many transcriptomes of mice exposed to a series of chemicals to elucidate the molecular mechanism of toxicity and to develop toxicity prediction technology. Acute toxicity of a chemical can be ...

BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning.

mAbs
Despite recent advances in transgenic animal models and display technologies, humanization of mouse sequences remains one of the main routes for therapeutic antibody development. Traditionally, humanization is manual, laborious, and requires expert k...

GRNUlar: A Deep Learning Framework for Recovering Single-Cell Gene Regulatory Networks.

Journal of computational biology : a journal of computational molecular cell biology
We propose GRNUlar, a novel deep learning framework for supervised learning of gene regulatory networks (GRNs) from single-cell RNA-Sequencing (scRNA-Seq) data. Our framework incorporates two intertwined models. First, we leverage the expressive abil...

scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network.

Nucleic acids research
Advances in single-cell RNA sequencing (scRNA-seq) have furthered the simultaneous classification of thousands of cells in a single assay based on transcriptome profiling. In most analysis protocols, single-cell type annotation relies on marker genes...

AxonDeep: Automated Optic Nerve Axon Segmentation in Mice With Deep Learning.

Translational vision science & technology
PURPOSE: Optic nerve damage is the principal feature of glaucoma and contributes to vision loss in many diseases. In animal models, nerve health has traditionally been assessed by human experts that grade damage qualitatively or manually quantify axo...