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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Efficient Drug-Pathway Association Analysis via Integrative Penalized Matrix Decomposition
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Efficient Drug-Pathway Association Analysis via Integrative Penalized Matrix Decomposition

机译：通过综合惩罚矩阵分解进行有效的药物-途径关联分析

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摘要

Traditional drug discovery practice usually follows the “one drug - one target” approach, seeking to identify drug molecules that act on individual targets, which ignores the systemic nature of human diseases. Pathway-based drug discovery recently emerged as an appealing approach to overcome this limitation. An important first step of such pathway-based drug discovery is to identify associations between drug molecules and biological pathways. This task has been made feasible by the accumulating data from high-throughput transcription and drug sensitivity profiling. In this paper, we developed “iPaD”, an integrative P enalized Matrix Decomposition method to identify drug-pathway associations through jointly modeling of such high-throughput transcription and drug sensitivity data. A scalable bi-convex optimization algorithm was implemented and gave iPaD tremendous advantage in computational efficiency over current state-of-the-art method, which allows it to handle the ever-growing large-scale data sets that current method cannot afford to. On two widely used real data sets, iPaD also significantly outperformed the current method in terms of the number of validated drug-pathway associations that were identified. The Matlab code of our algorithm publicly available at http://licong-jason.github.io/iPaD/
机译：传统的药物发现实践通常遵循“一种药物-一个目标”的方法，试图识别作用于单个目标的药物分子，而忽略了人类疾病的系统性。最近，基于途径的药物发现成为克服这一局限性的一种有吸引力的方法。此类基于途径的药物发现的重要第一步是确定药物分子与生物学途径之间的关联。通过积累来自高通量转录和药物敏感性分析的数据，使此任务变得可行。在本文中，我们开发了“ iPaD”，这是一种集成的P格式化矩阵分解方法，可以通过对此类高通量转录和药物敏感性数据进行联合建模来识别药物-途径关联。实施了可扩展的双凸优化算法，与当前的最新方法相比，iPaD在计算效率上具有巨大优势，这使它能够处理当前方法无法承受的，不断增长的大规模数据集。在两个被广泛使用的真实数据集上，iPaD在确定的经过验证的药物-途径关联的数量上也大大优于当前方法。我们算法的Matlab代码可在http://licong-jason.github.io/iPaD/上公开获得

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