AIMC Topic: Mitochondria
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Bayesian-optimized deep learning for identifying essential genes of mitophagy and fostering therapies to combat drug resistance in human cancers.
Journal of cellular and molecular medicine
Jan 1, 2025
Dysregulated mitophagy is essential for mitochondrial quality control within human cancers. However, identifying hub genes regulating mitophagy and developing mitophagy-based treatments to combat drug resistance remains challenging. Herein, BayeDEM (...
Immunoinfiltration Analysis of Mitochondrial Damage-Related Genes in Lung Adenocarcinoma and Construction of a Classification and Prognostic Model Integrated With WGCNA and Machine Learning Algorithms.
Cancer medicine
Jan 1, 2025
BACKGROUND: Lung adenocarcinoma (LUAD) exhibits molecular heterogeneity, with mitochondrial damage affecting progression. The relationship between mitochondrial damage and immune infiltration, and Weighted Gene Co-expression Network Analysis (WGCNA)-...
AI-recognized mitochondrial phenotype enables identification of drug targets.
Nature computational science
Aug 1, 2024
[An artificial neural network diagnostic model for scleroderma and immune cell infiltration analysis based on mitochondria-associated genes].
Nan fang yi ke da xue xue bao = Journal of Southern Medical University
May 20, 2024
OBJECTIVE: To establish a diagnostic model for scleroderma by combining machine learning and artificial neural network based on mitochondria-related genes.
Deep-learning based flat-fielding quantitative phase contrast microscopy.
Optics express
Mar 25, 2024
Quantitative phase contrast microscopy (QPCM) can realize high-quality imaging of sub-organelles inside live cells without fluorescence labeling, yet it requires at least three phase-shifted intensity images. Herein, we combine a novel convolutional ...
Instance segmentation of mitochondria in electron microscopy images with a generalist deep learning model trained on a diverse dataset.
Cell systems
Jan 18, 2023
Mitochondria are extremely pleomorphic organelles. Automatically annotating each one accurately and precisely in any 2D or volume electron microscopy (EM) image is an unsolved computational challenge. Current deep learning-based approaches train mode...
Evaluation of Image Classification for Quantifying Mitochondrial Morphology Using Deep Learning.
Endocrine, metabolic & immune disorders drug targets
Jan 1, 2023
BACKGROUND: Mitochondrial morphology reversibly changes between fission and fusion. As these changes (mitochondrial dynamics) reflect the cellular condition, they are one of the simplest indicators of cell state and predictors of cell fate. However, ...
Fear memory-associated synaptic and mitochondrial changes revealed by deep learning-based processing of electron microscopy data.
Cell reports
Aug 2, 2022
Serial section electron microscopy (ssEM) can provide comprehensive 3D ultrastructural information of the brain with exceptional computational cost. Targeted reconstruction of subcellular structures from ssEM datasets is less computationally demandin...
High-Throughput Image Analysis of Lipid-Droplet-Bound Mitochondria.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2021
Changes to mitochondrial architecture are associated with various adaptive and pathogenic processes. However, quantification of changes to mitochondrial structures is limited by the yet unmet challenge of defining the borders of each individual mitoc...
mRNALoc: a novel machine-learning based in-silico tool to predict mRNA subcellular localization.
Nucleic acids research
Jul 2, 2020
Recent evidences suggest that the localization of mRNAs near the subcellular compartment of the translated proteins is a more robust cellular tool, which optimizes protein expression, post-transcriptionally. Retention of mRNA in the nucleus can regul...
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