AIMC Topic: Mice

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Label Alignment Improves EEG-based Machine Learning-based Classification of Traumatic Brain Injury.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Machine learning and deep learning algorithms have paved the way for improved analysis of biomedical data which has led to a better understanding of various biological conditions. However, one major hindrance to leveraging the potential of machine le...

CellDART: cell type inference by domain adaptation of single-cell and spatial transcriptomic data.

Nucleic acids research
Deciphering the cellular composition in genome-wide spatially resolved transcriptomic data is a critical task to clarify the spatial context of cells in a tissue. In this study, we developed a method, CellDART, which estimates the spatial distributio...

Improving confidence in lipidomic annotations by incorporating empirical ion mobility regression analysis and chemical class prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Mass spectrometry-based untargeted lipidomics aims to globally characterize the lipids and lipid-like molecules in biological systems. Ion mobility increases coverage and confidence by offering an additional dimension of separation and a ...

Framework for denoising Monte Carlo photon transport simulations using deep learning.

Journal of biomedical optics
SIGNIFICANCE: The Monte Carlo (MC) method is widely used as the gold-standard for modeling light propagation inside turbid media, such as human tissues, but combating its inherent stochastic noise requires one to simulate a large number photons, resu...

3D time-lapse imaging of a mouse embryo using intensity diffraction tomography embedded inside a deep learning framework.

Applied optics
We present a compact 3D diffractive microscope that can be inserted directly in a cell incubator for long-term observation of developing organisms. Our setup is particularly simple and robust, since it does not include any moving parts and is compati...

DNAcycP: a deep learning tool for DNA cyclizability prediction.

Nucleic acids research
DNA mechanical properties play a critical role in every aspect of DNA-dependent biological processes. Recently a high throughput assay named loop-seq has been developed to quantify the intrinsic bendability of a massive number of DNA fragments simult...

Spatial resolution improved fluorescence lifetime imaging via deep learning.

Optics express
We present a deep learning approach to obtain high-resolution (HR) fluorescence lifetime images from low-resolution (LR) images acquired from fluorescence lifetime imaging (FLIM) systems. We first proposed a theoretical method for training neural net...

massNet: integrated processing and classification of spatially resolved mass spectrometry data using deep learning for rapid tumor delineation.

Bioinformatics (Oxford, England)
MOTIVATION: Mass spectrometry imaging (MSI) provides rich biochemical information in a label-free manner and therefore holds promise to substantially impact current practice in disease diagnosis. However, the complex nature of MSI data poses computat...

Deep learning quantification of vascular pharmacokinetic parameters in mouse brain tumor models.

Frontiers in bioscience (Landmark edition)
BACKGROUND: Dynamic contrast-enhanced (DCE) MRI is widely used to assess vascular perfusion and permeability in cancer. In small animal applications, conventional modeling of pharmacokinetic (PK) parameters from DCE MRI images is complex and time con...

A hybrid deep learning framework for gene regulatory network inference from single-cell transcriptomic data.

Briefings in bioinformatics
Inferring gene regulatory networks (GRNs) based on gene expression profiles is able to provide an insight into a number of cellular phenotypes from the genomic level and reveal the essential laws underlying various life phenomena. Different from the ...