ArticleScientific reports2018
Deep Analysis of Mitochondria and Cell Health Using Machine Learning.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
36 citing papers in PubMed.
- Protocol update to: Protocol for rapid manipulation of mitochondrial morphology in living cells using inducible counter mitochondrial morphology.STAR protocols · 2026Article
- From Microscopy to Nanoscopy: Contemporary Physical Methods in Mitochondrial Structural Biology.International journal of molecular sciences · 2026Review
- Systematic evaluation of mitochondrial morphology regulators for amelioration of neuronal α-synucleinopathy.NPJ Parkinson's disease · 2026Article
- MitoTex (Mitochondria Texture Analysis User Interface): Open-Source Framework for Textural Characterization and Classification of Mitochondrial Structures.International journal of molecular sciences · 2026Article
- Deep learning analysis of urine-derived stem cell mitochondrial morphology as a non-invasive Alzheimer's disease biomarker.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2026Article
- C1q drives neural stem cell quiescence by regulating cell cycle and metabolism through BAI1.Nature communications · 2025Article
- Deep Learning-Based Automatic Segmentation and Analysis of Mitochondrial Damage by Zika Virus and SARS-CoV-2.Viruses · 2025Article
- A fully automated morphological analysis of yeast mitochondria from wide-field fluorescence images.Scientific reports · 2024Article
- Review
- Dynamic death decisions: How mitochondrial dynamics shape cellular commitment to apoptosis and ferroptosis.Developmental cell · 2024Review
- An algorithm-based technique for counting mitochondria in cells using immunohistochemical staining of formalin-fixed and paraffin-embedded sections.Journal of cancer research and clinical oncology · 2024Article
- Iterative pseudo balancing for stem cell microscopy image classification.Scientific reports · 2024Article
- Morphological multiparameter filtration and persistent homology in mitochondrial image analysis.PloS one · 2024Article
- Single molecule studies of dynamic platelet interactions with endothelial cells.Frontiers in bioengineering and biotechnology · 2024Article
- Mitochondrial heterogeneity in diseases.Signal transduction and targeted therapy · 2023Review
- Article
- Nano-Sized Selenium Maintains Performance and Improves Health Status and Antioxidant Potential While Not Compromising Ultrastructure of Breast Muscle and Liver in Chickens.Antioxidants (Basel, Switzerland) · 2023Article
- Quantitative assessment of mitochondrial morphology relevant for studies on cellular health and environmental toxicity.Computational and structural biotechnology journal · 2023Article
- Mitochondrial network expansion and dynamic redistribution during islet morphogenesis in zebrafish larvae.FEBS letters · 2023Article
- A two-tier feature selection method using Coalition game and Nystrom sampling for screening COVID-19 from chest X-Ray images.Journal of ambient intelligence and humanized computing · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
There is a critical need for better analytical methods to study mitochondria in normal and diseased states. Mitochondrial image analysis is typically done on still images using slow manual methods or automated methods of limited types of features. MitoMo integrated software overcomes these bottlenecks by automating rapid unbiased quantitative analysis of mitochondrial morphology, texture, motion, and morphogenesis and advances machine-learning classification to predict cell health by combining features. Our pixel-based approach for motion analysis evaluates the magnitude and direction of motion of: (1) molecules within mitochondria, (2) individual mitochondria, and (3) distinct morphological classes of mitochondria. MitoMo allows analysis of mitochondrial morphogenesis in time-lapse videos to study early progression of cellular stress. Biological applications are presented including: (1) establishing normal phenotypes of mitochondria in different cell types; (2) quantifying stress-induced mitochondrial hyperfusion in cells treated with an environmental toxicant, (3) tracking morphogenesis in mitochondria undergoing swelling, and (4) evaluating early changes in cell health when morphological abnormalities are not apparent. MitoMo unlocks new information on mitochondrial phenotypes and dynamics by enabling deep analysis of mitochondrial features in any cell type and can be applied to a broad spectrum of research problems in cell biology, drug testing, toxicology, and medicine.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.