AIMC Topic: Mice
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Identification of hub biomarkers in liver post-metabolic and bariatric surgery using comprehensive machine learning (experimental studies).
International journal of surgery (London, England)
Feb 1, 2025
BACKGROUND: The global prevalence of non-alcoholic fatty liver disease (NAFLD) is approximately 30%, and the condition can progress to non-alcoholic steatohepatitis, cirrhosis, and hepatocellular carcinoma. Metabolic and bariatric surgery (MBS) has b...
Fluo-Cast-Bright: a deep learning pipeline for the non-invasive prediction of chromatin structure and developmental potential in live oocytes.
Communications biology
Jan 29, 2025
In mammalian oocytes, large-scale chromatin organization regulates transcription, nuclear architecture, and maintenance of chromosome stability in preparation for meiosis onset. Pre-ovulatory oocytes with distinct chromatin configurations exhibit pro...
A deep learning pipeline for three-dimensional brain-wide mapping of local neuronal ensembles in teravoxel light-sheet microscopy.
Nature methods
Jan 27, 2025
Teravoxel-scale, cellular-resolution images of cleared rodent brains acquired with light-sheet fluorescence microscopy have transformed the way we study the brain. Realizing the potential of this technology requires computational pipelines that gener...
Identification and validation of key autophagy-related genes in lupus nephritis by bioinformatics and machine learning.
PloS one
Jan 27, 2025
INTRODUCTION: Lupus nephritis (LN) is one of the most frequent and serious organic manifestations of systemic lupus erythematosus (SLE). Autophagy, a new form of programmed cell death, has been implicated in a variety of renal diseases, but the relat...
Integrating Retinal Segmentation Metrics with Machine Learning for Predictions from Mouse SD-OCT Scans.
Current eye research
Jan 23, 2025
PURPOSE: This study aimed to initially test whether machine learning approaches could categorically predict two simple biological features, mouse age and mouse species, using the retinal segmentation metrics.
KanCell: dissecting cellular heterogeneity in biological tissues through integrated single-cell and spatial transcriptomics.
Journal of genetics and genomics = Yi chuan xue bao
Jan 23, 2025
KanCell is a deep learning model based on Kolmogorov-Arnold networks (KAN) designed to enhance cellular heterogeneity analysis by integrating single-cell RNA sequencing and spatial transcriptomics (ST) data. ST technologies provide insights into gene...
Mapping the topography of spatial gene expression with interpretable deep learning.
Nature methods
Jan 23, 2025
Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of these data complicates analysis of spatial gene expression patterns. We address this issue by deriving a to...
Feature selection enhances peptide binding predictions for TCR-specific interactions.
Frontiers in immunology
Jan 23, 2025
INTRODUCTION: T-cell receptors (TCRs) play a critical role in the immune response by recognizing specific ligand peptides presented by major histocompatibility complex (MHC) molecules. Accurate prediction of peptide binding to TCRs is essential for a...
Robotic Fast Dual-Arm Patch Clamp System for Mechanosensitive Excitability Research of Neurons.
IEEE transactions on bio-medical engineering
Jan 21, 2025
OBJECTIVE: A robotic fast dual-arm patch clamp system with controllable mechanical stimulation is proposed in this paper for mechanosensitive excitability research of neurons in brain slice.
iDIA-QC: AI-empowered data-independent acquisition mass spectrometry-based quality control.
Nature communications
Jan 21, 2025
Quality control (QC) in mass spectrometry (MS)-based proteomics is mainly based on data-dependent acquisition (DDA) analysis of standard samples. Here, we collect 2754 files acquired by data independent acquisition (DIA) and paired 2638 DDA files fro...
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