AIMC Topic: Mice

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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...
Mice Computational Biology Sequence Analysis, RNA Transcriptome Deep Learning Single-Cell Analysis Humans Animals Gene Expression Profiling
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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...
Humans Mice Transcriptome Tumor Microenvironment Deep Learning Neural Networks, Computer Animals Algorithms Gene Expression Profiling Brain
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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...
Mice Peptides Peptide Library Receptors, Antigen, T-Cell Protein Binding Machine Learning Animals
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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.
Equipment Design Neurons Patch-Clamp Techniques Animals Mice Robotics
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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...
Animals Mice Mass Spectrometry Artificial Intelligence Proteomics Tandem Mass Spectrometry Liver Software Quality Control Chromatography, Liquid
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Genomic and algorithm-based predictive risk assessment models for benzene exposure.

Frontiers in public health Jan 21, 2025
AIM: In this research, we leveraged bioinformatics and machine learning to pinpoint key risk genes associated with occupational benzene exposure and to construct genomic and algorithm-based predictive risk assessment models.
Occupational Exposure Humans Genomics Machine Learning Algorithms Risk Assessment Animals Mice Benzene
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Efficient recognition of Parkinson's disease mice on stepping characters with CNN.

Scientific reports Jan 20, 2025
Parkinson's disease (PD), as the second most prevalent neurodegenerative disorder worldwide, impacts the quality of life for over 12 million patients. This study aims to enhance the accuracy of early diagnosis of PD through non-invasive methods, with...
Mice Disease Models, Animal Mice, Inbred C57BL Parkinson Disease Algorithms Neural Networks, Computer Early Diagnosis Animals Image Processing, Computer-Assisted
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Analyzing the relationship between gene expression and phenotype in space-flown mice using a causal inference machine learning ensemble.

Scientific reports Jan 18, 2025
Spaceflight has several detrimental effects on human and rodent health. For example, liver dysfunction is a common phenotype observed in space-flown rodents, and this dysfunction is partially reflected in transcriptomic changes. Studies linking trans...
Animals Gene Expression Profiling Transcriptome Machine Learning Phenotype Mice Liver Space Flight
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Explainable deep learning and virtual evolution identifies antimicrobial peptides with activity against multidrug-resistant human pathogens.

Nature microbiology Jan 17, 2025
Artificial intelligence (AI) is a promising approach to identify new antimicrobial compounds in diverse microbial species. Here we developed an AI-based, explainable deep learning model, EvoGradient, that predicts the potency of antimicrobial peptide...
Antimicrobial Peptides Mice Klebsiella pneumoniae Bacteria Escherichia coli Acinetobacter baumannii Enterococcus faecium Deep Learning Microbial Sensitivity Tests Drug Resistance, Multiple, Bacterial Anti-Bacterial Agents Animals Humans
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Biologically relevant integration of transcriptomics profiles from cancer cell lines, patient-derived xenografts, and clinical tumors using deep learning.

Science advances Jan 17, 2025
Cell lines and patient-derived xenografts are essential to cancer research; however, the results derived from such models often lack clinical translatability, as they do not fully recapitulate the complex cancer biology. Identifying preclinical model...
Humans Animals Neoplasms Gene Expression Regulation, Neoplastic Deep Learning Gene Expression Profiling Xenograft Model Antitumor Assays Cell Line, Tumor Transcriptome Heterografts Mice
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