融合梯度提升与支持向量机，高效预测小核仁RNA与疾病关联
GBDTSVM: Combined Support Vector Machine and Gradient Boosting Decision Tree Framework for efficient snoRNA-disease association prediction
- 用GBDT提取特征，SVM分类，构建双模型协同预测框架
- 在MDRF数据集上达到AUROC 0.96、AUPRC 0.95的高精度
- 适用于生物医学领域研究人员快速筛选潜在snoRNA-疾病关联
小核仁RNA（snoRNAs）在多种人类疾病的发病机制和表型特征中起关键作用，因此精确识别snoRNA-疾病关联（SDAs）对疾病研究和治疗策略推进至关重要。传统实验方法成本高、耗时长，机器学习计算方法成为可行替代方案。本文提出名为GBDTSVM的新模型，结合梯度提升决策树（GBDT）与支持向量机（SVM），通过GBDT提取snoRNA与疾病整合特征表示，再由SVM进行分类识别潜在关联。同时引入高斯核相似性来增强预测准确性。在MDRF数据集上的实验表明，GBDTSVM性能优于现有先进方法，达到AUROC 0.96、AUPRC 0.95；在LSGT和PsnoD两个数据集上也表现优异。对九种常见疾病的前十名预测结果进行案例验证，进一步证明了该方法的有效性。该模型可作为推进snoRNA相关疾病研究的强大工具。源代码与数据集可通过https://github.com/mariamuna04/gbdtsvm获取。
原文摘要 · Abstract (English)
Small nucleolar RNAs (snoRNAs) are increasingly recognized for their critical role in the pathogenesis and characterization of various human diseases. Consequently, the precise identification of snoRNA-disease associations (SDAs) is essential for the progression of diseases and the advancement of treatment strategies. However, conventional biological experimental approaches are costly, time-consuming, and resource-intensive; therefore, machine learning-based computational methods offer a promising solution to mitigate these limitations. This paper proposes a model called 'GBDTSVM', representing a novel and efficient machine learning approach for predicting snoRNA-disease associations by leveraging a Gradient Boosting Decision Tree (GBDT) and Support Vector Machine (SVM). 'GBDTSVM' effectively extracts integrated snoRNA-disease feature representations utilizing GBDT and SVM is subsequently utilized to classify and identify potential associations. Furthermore, the method enhances the accuracy of these predictions by incorporating Gaussian kernel profile similarity for both snoRNAs and diseases. Experimental evaluation of the GBDTSVM model demonstrated superior performance compared to state-of-the-art methods in the field, achieving an area under the receiver operating characteristic (AUROC) of 0.96 and an area under the precision-recall curve (AUPRC) of 0.95 on MDRF dataset. Moreover, our model shows superior performance on two more datasets named LSGT and PsnoD. Additionally, a case study on the predicted snoRNA-disease associations verified the top 10 predicted snoRNAs across nine prevalent diseases, further validating the efficacy of the GBDTSVM approach. These results underscore the model's potential as a robust tool for advancing snoRNA-related disease research. Source codes and datasets our proposed framework can be obtained from: https://github.com/mariamuna04/gbdtsvm
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