ArticleInternational journal of molecular sciences2025
Supervised Machine-Based Learning and Computational Analysis to Reveal Unique Molecular Signatures Associated with Wound Healing and Fibrotic Outcomes to Lens Injury.
Article in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
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Who cites it
4 citing papers in PubMed.
- Multifaceted mechanistic exploration ofFrontiers in cell and developmental biology · 2026Article
- Distinct transcriptional regulation of the Wnt and TGFβ signaling families associated with wound healing and fibrotic outcomes to lens injury.Experimental eye research · 2025Article
- Artificial Intelligence in Ocular Transcriptomics: Applications of Unsupervised and Supervised Learning.Cells · 2025Review
- Betanin Promotes Wound Closure and Drives a Context-Specific Transcriptional Repair Program in HaCaT Keratinocytes: A Multi-Evidence AI-Guided Prioritization Study.BioFactors (Oxford, England)Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Posterior capsule opacification (PCO), a frequent complication of cataract surgery, arises from dysregulated wound healing and fibrotic transformation of residual lens epithelial cells. While transcriptomic and machine learning (ML) approaches have elucidated fibrosis-related pathways in other tissues, the molecular divergence between regenerative and fibrotic outcomes in the lens remains unclear. Here, we used an ex vivo chick lens injury model to simulate post-surgical conditions, collecting RNA from lenses undergoing either regenerative wound healing or fibrosis between days 1-3 post-injury. Bulk RNA sequencing data were normalized, log-transformed, and subjected to univariate filtering prior to training LASSO, SVM, and RF ML models to identify discriminatory gene signatures. Each model was independently validated using a held-out test set. Distinct gene sets were identified, including fibrosis-associated genes (
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Registered trials
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