ArticlePloS one2023
Potential of GJA8 gene variants in predicting age-related cataract: A comparison of supervised machine learning methods.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed, 3 citations in OpenAlex.
- Through theGenes · 2024Review
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cataracts are the problems associated with the crystallins proteins of the eye lens. Any perturbation in the conformity of these proteins results in a cataract. Age-related cataract is the most common type among all cataracts as it accounts for almost 80% of cases of senile blindness worldwide. This research study was performed to predict the role of single nucleotide polymorphisms (SNPs) of the GJA8 gene with age-related cataracts in 718 subjects (400 age-related cataract patients and 318 healthy individuals). A comparison of supervised machine learning classification algorithm including logistic regression (LR), random forest (RF) and Artificial Neural Network (ANN) were presented to predict the age-related cataracts. The results indicated that LR is the best for predicting age-related cataracts. This successfully developed model after accounting different genetic and demographic factors to predict cataracts will help in effective disease management and decision-making medical practitioner and experts.
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