用贝叶斯变量选择提升药物发现的预测精度与可解释性
BVSIMC: Bayesian Variable Selection-Guided Inductive Matrix Completion for Improved and Interpretable Drug Discovery
- 基于贝叶斯框架自动筛选相关药物和疾病特征
- 在结核耐药预测和药物重定位中均超越现有方法
- 能识别临床有意义的关键特征，适合医药研究者使用
近期药物发现研究显示，整合药物化学性质和疾病基因信息等辅助信息可显著提升预测性能。然而这些辅助特征相关性差异大，常存在噪声且维度高。本文提出贝叶斯变量选择引导的归纳矩阵补全（BVSIMC），一种新式贝叶斯模型，可在药物发现中自动筛选辅助特征。通过学习稀疏潜在表示，BVSIMC同时提升预测准确率与结果可解释性。我们在模拟实验及两个真实应用中验证该方法：1）结核分枝杆菌耐药性预测；2）计算药物重定位中的新药-病关联预测。在合成数据与真实数据上，BVSIMC均优于多种先进方法。在两个实际案例中，BVSIMC还揭示了最具临床意义的辅助特征。
原文摘要 · Abstract (English)
Recent advances in drug discovery have demonstrated that incorporating side information (e.g., chemical properties about drugs and genomic information about diseases) often greatly improves prediction performance. However, these side features can vary widely in relevance and are often noisy and high-dimensional. We propose Bayesian Variable Selection-Guided Inductive Matrix Completion (BVSIMC), a new Bayesian model that enables variable selection from side features in drug discovery. By learning sparse latent embeddings, BVSIMC improves both predictive accuracy and interpretability. We validate our method through simulation studies and two drug discovery applications: 1) prediction of drug resistance in Mycobacterium tuberculosis, and 2) prediction of new drug-disease associations in computational drug repositioning. On both synthetic and real data, BVSIMC outperforms several other state-of-the-art methods in terms of prediction. In our two real examples, BVSIMC further reveals the most clinically meaningful side features.
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