Machine Learning-Based Prediction of Key Genes Correlated to the Subretinal Lesion Severity in a Mouse Model of Age-Related Macular Degeneration
<|reference_start|>Machine Learning-Based Prediction of Key Genes Correlated to the Subretinal Lesion Severity in a Mouse Model of Age-Related Macular Degeneration: Age-related macular degeneration (AMD) is a major cause of blindness in older adults, severely affecting vision and quality of life. Despite advances in understanding AMD, the molecular factors driving the severity of subretinal scarring (fibrosis) remain elusive, hampering the development of effective therapies. This study introduces a machine learning-based framework to predict key genes that are strongly correlated with lesion severity and to identify potential therapeutic targets to prevent subretinal fibrosis in AMD. Using an original RNA sequencing (RNA-seq) dataset from the diseased retinas of JR5558 mice, we developed a novel and specific feature engineering technique, including pathway-based dimensionality reduction and gene-based feature expansion, to enhance prediction accuracy. Two iterative experiments were conducted by leveraging Ridge and ElasticNet regression models to assess biological relevance and gene impact. The results highlight the biological significance of several key genes and demonstrate the framework's effectiveness in identifying novel therapeutic targets. The key findings provide valuable insights for advancing drug discovery efforts and improving treatment strategies for AMD, with the potential to enhance patient outcomes by targeting the underlying genetic mechanisms of subretinal lesion development.<|reference_end|>
@article{yan2024machine,
    title={Machine Learning-Based Prediction of Key Genes Correlated to the
  Subretinal Lesion Severity in a Mouse Model of Age-Related Macular
  Degeneration},
    author={Kuan Yan, Yue Zeng, Dai Shi, Ting Zhang, Dmytro Matsypura, Mark C.
  Gillies, Ling Zhu, Junbin Gao},
    journal={arXiv preprint arXiv:2409.05047},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.05047},
    primaryClass={q-bio.GN cs.LG}
}