AIMC Topic: Mice

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JSOM: Jointly-evolving self-organizing maps for alignment of biological datasets and identification of related clusters.

PLoS computational biology
With the rapid advances of various single-cell technologies, an increasing number of single-cell datasets are being generated, and the computational tools for aligning the datasets which make subsequent integration or meta-analysis possible have beco...

DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires.

Nature communications
Deep learning algorithms have been utilized to achieve enhanced performance in pattern-recognition tasks. The ability to learn complex patterns in data has tremendous implications in immunogenomics. T-cell receptor (TCR) sequencing assesses the diver...

Deep learning based neuronal soma detection and counting for Alzheimer's disease analysis.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Alzheimer's Disease (AD) is associated with neuronal damage and decrease. Micro-Optical Sectioning Tomography (MOST) provides an approach to acquire high-resolution images for neuron analysis in the whole-brain. Application ...

Astrocyte regional heterogeneity revealed through machine learning-based glial neuroanatomical assays.

The Journal of comparative neurology
Evaluation of reactive astrogliosis by neuroanatomical assays represents a common experimental outcome for neuroanatomists. The literature demonstrates several conflicting results as to the accuracy of such measures. We posited that the diverging res...

Deep learning-based enhancement of epigenomics data with AtacWorks.

Nature communications
ATAC-seq is a widely-applied assay used to measure genome-wide chromatin accessibility; however, its ability to detect active regulatory regions can depend on the depth of sequencing coverage and the signal-to-noise ratio. Here we introduce AtacWorks...

Machine learning-based classification of mitochondrial morphology in primary neurons and brain.

Scientific reports
The mitochondrial network continually undergoes events of fission and fusion. Under physiologic conditions, the network is in equilibrium and is characterized by the presence of both elongated and punctate mitochondria. However, this balanced, homeos...

DNN-m6A: A Cross-Species Method for Identifying RNA N6-Methyladenosine Sites Based on Deep Neural Network with Multi-Information Fusion.

Genes
As a prevalent existing post-transcriptional modification of RNA, N6-methyladenosine (m6A) plays a crucial role in various biological processes. To better radically reveal its regulatory mechanism and provide new insights for drug design, the accurat...

Deep learning differentiates between healthy and diabetic mouse ears from optical coherence tomography angiography images.

Annals of the New York Academy of Sciences
We trained a deep learning algorithm to use skin optical coherence tomography (OCT) angiograms to differentiate between healthy and type 2 diabetic mice. OCT angiograms were acquired with a custom-built OCT system based on an akinetic swept laser at ...

Deep Learning for Automatic Segmentation of Hybrid Optoacoustic Ultrasound (OPUS) Images.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
The highly complementary information provided by multispectral optoacoustics and pulse-echo ultrasound have recently prompted development of hybrid imaging instruments bringing together the unique contrast advantages of both modalities. In the hybrid...