Polyadic Regression and its Application to Chemogenomics
Polyadic Regression and its Application to Chemogenomics
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DOI：
10.1137/1.9781611974973.9
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发表时间：
2017
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影响因子：
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通讯作者：
Ioakeim Perros;Fei Wang;Ping Zhang;Peter Walker;R. Vuduc;Jyotishman Pathak;Jimeng Sun
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作者：
Ioakeim Perros;Fei Wang;Ping Zhang;Peter Walker;R. Vuduc;Jyotishman Pathak;Jimeng Sun
我们研究的问题的多元预测，其中的输入包括一个有序的元组的对象，目标是预测与它们相关联的测量。许多任务可以很自然地被定义为多元预测问题。例如，在药物发现中，重要的是评估药物对各种组织特异性疾病的治疗效果，因为它在可用基因上表达。因此，我们基本上预测了几个（药物，基因，组织）三元组的表达值测量。为了解决多元预测问题，我们提出了一个通用框架，称为多元回归，预测与多个对象相关的测量。我们的框架是归纳的，在这个意义上，使预测新的对象，在训练过程中看不到。我们的模型是富有表现力的，以有效的方式探索高阶，多元相互作用。一个交替的近端梯度下降过程，提出了适合我们的模型。我们使用真实世界的化学基因组学数据进行了广泛的评估，其中我们说明了多元回归的上级性能超过现有技术。我们的方法实现了预测和实际测量向量之间的斯皮尔曼相关性增加0.06和0.1，分别用于预测缺失的多元数据和预测新药的多元数据。
We study the problem of Polyadic Prediction, where the input consists of an ordered tuple of objects, and the goal is to predict a measurement associated with them. Many tasks can be naturally framed as Polyadic Prediction problems. In drug discovery, for instance, it is important to estimate the treatment effect of a drug on various tissue-specific diseases, as it is expressed over the available genes. Thus, we essentially predict the expression value measurements for several (drug, gene, tissue) triads. To tackle Polyadic Prediction problems, we propose a general framework, called Polyadic Regression, predicting measurements associated with multiple objects. Our framework is inductive, in the sense of enabling predictions for new objects, unseen during training. Our model is expressive, exploring high-order, polyadic interactions in an efficient manner. An alternating Proximal Gradient Descent procedure is proposed to fit our model. We perform an extensive evaluation using real-world chemogenomics data, where we illustrate the superior performance of Polyadic Regression over the prior art. Our method achieves an increase of 0.06 and 0.1 in Spearman correlation between the predicted and the actual measurement vectors, for predicting missing polyadic data and predicting polyadic data for new drugs, respectively.