Bolgár Bence, Antal Péter
Department of Measurement and Information Systems, Budapest University of Technology and Economics, Magyar tudósok krt. 2., Budapest, 1117, Hungary.
BMC Bioinformatics. 2017 Oct 4;18(1):440. doi: 10.1186/s12859-017-1845-z.
Computational fusion approaches to drug-target interaction (DTI) prediction, capable of utilizing multiple sources of background knowledge, were reported to achieve superior predictive performance in multiple studies. Other studies showed that specificities of the DTI task, such as weighting the observations and focusing the side information are also vital for reaching top performance.
We present Variational Bayesian Multiple Kernel Logistic Matrix Factorization (VB-MK-LMF), which unifies the advantages of (1) multiple kernel learning, (2) weighted observations, (3) graph Laplacian regularization, and (4) explicit modeling of probabilities of binary drug-target interactions.
VB-MK-LMF achieves significantly better predictive performance in standard benchmarks compared to state-of-the-art methods, which can be traced back to multiple factors. The systematic evaluation of the effect of multiple kernels confirm their benefits, but also highlights the limitations of linear kernel combinations, already recognized in other fields. The analysis of the effect of prior kernels using varying sample sizes sheds light on the balance of data and knowledge in DTI tasks and on the rate at which the effect of priors vanishes. This also shows the existence of "small sample size" regions where using side information offers significant gains. Alongside favorable predictive performance, a notable property of MF methods is that they provide a unified space for drugs and targets using latent representations. Compared to earlier studies, the dimensionality of this space proved to be surprisingly low, which makes the latent representations constructed by VB-ML-LMF especially well-suited for visual analytics. The probabilistic nature of the predictions allows the calculation of the expected values of hits in functionally relevant sets, which we demonstrate by predicting drug promiscuity. The variational Bayesian approximation is also implemented for general purpose graphics processing units yielding significantly improved computational time.
In standard benchmarks, VB-MK-LMF shows significantly improved predictive performance in a wide range of settings. Beyond these benchmarks, another contribution of our work is highlighting and providing estimates for further pharmaceutically relevant quantities, such as promiscuity, druggability and total number of interactions.
据报道，能够利用多种背景知识来源的药物-靶点相互作用（DTI）预测计算融合方法在多项研究中取得了卓越的预测性能。其他研究表明，DTI任务的特异性，如对观测值加权和关注辅助信息，对于达到最佳性能也至关重要。
我们提出了变分贝叶斯多核逻辑矩阵分解（VB-MK-LMF）方法，该方法统一了以下优点：（1）多核学习；（2）加权观测值；（3）图拉普拉斯正则化；（4）对二元药物-靶点相互作用概率进行显式建模。
与现有最先进的方法相比，VB-MK-LMF在标准基准测试中实现了显著更好的预测性能，这可追溯到多个因素。对多核效果的系统评估证实了它们的益处，但也凸显了线性核组合的局限性，这在其他领域已得到认可。使用不同样本量对先验核效果的分析揭示了DTI任务中数据与知识的平衡以及先验效果消失的速率。这也表明存在“小样本量”区域，在这些区域使用辅助信息能带来显著收益。除了良好的预测性能外，矩阵分解方法的一个显著特性是它们使用潜在表示为药物和靶点提供了一个统一的空间。与早期研究相比，这个空间的维度被证明出奇地低，这使得VB-ML-LMF构建的潜在表示特别适合视觉分析。预测的概率性质允许计算功能相关集合中命中的期望值，我们通过预测药物多效性来证明这一点。变分贝叶斯近似也在通用图形处理单元上实现，显著提高了计算时间。
在标准基准测试中，VB-MK-LMF在广泛的设置下显示出显著提高的预测性能。除了这些基准测试外，我们工作的另一个贡献是突出并提供了对其他药学相关量的估计，如多效性、可成药性和相互作用总数。
