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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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SGLMDA: A Subgraph Learning-Based Method for miRNA-Disease Association Prediction.

Cunmei Ji, Ning Yu, Yutian Wang

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |March 6, 2024
    PubMed
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    This study introduces SGLMDA, a novel subgraph learning method for predicting microRNA-disease associations. SGLMDA effectively identifies potential links, advancing disease mechanism understanding and aiding in the discovery of new therapeutic targets.

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    Area of Science:

    • Genomics
    • Bioinformatics
    • Computational Biology

    Background:

    • MicroRNAs (miRNAs) are key regulators of gene expression, and their dysregulation is linked to numerous human diseases.
    • Identifying miRNA-disease associations is crucial for understanding disease pathogenesis but experimental methods are costly and time-consuming.

    Purpose of the Study:

    • To develop a computational method for predicting miRNA-disease associations that overcomes limitations of existing approaches in large-scale networks.
    • To introduce a novel subgraph learning framework, SGLMDA, for robust and effective prediction of these associations.

    Main Methods:

    • SGLMDA samples K-hop subgraphs from a heterogeneous miRNA-disease graph.
    • A Graph Neural Network (GNN) is employed for feature extraction and prediction within these subgraphs.
    • The method was evaluated using 5-fold Cross-Validation on benchmark datasets like HMDD v2.0 and HMDD v3.2.

    Main Results:

    • SGLMDA demonstrates effective and robust prediction of potential miRNA-disease associations.
    • The method achieved superior performance compared to state-of-the-art techniques, evidenced by higher Area Under the Curve (AUC) and Average Precision (AP) values.
    • Case studies on Colon Neoplasms and Triple-Negative Breast Cancer (TNBC) validated the predictive capabilities of SGLMDA.

    Conclusions:

    • SGLMDA offers a powerful computational tool for predicting miRNA-disease associations.
    • The findings contribute to a deeper molecular understanding of diseases and can guide future research and therapeutic strategies.