Collaborative filtering on a family of biological targets

被引:79
作者
Erhan, D
L'Heureux, PJ
Yue, SY
Bengio, Y
机构
[1] Univ Montreal, Dept IRO, Montreal, PQ H3C 3J7, Canada
[2] AstraZeneca R&D Montreal, St Laurent, PQ H4S 1Z9, Canada
关键词
D O I
10.1021/ci050367t
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
Building a QSAR model of a new biological target for which few screening data are available is a statistical challenge. However, the new target may be part of a bigger family, for which we have more screening data. Collaborative filtering or, more generally, multi-task learning, is a machine learning approach that improves the generalization performance of an algorithm by using information from related tasks as an inductive bias. We use collaborative filtering techniques for building predictive models that link multiple targets to multiple examples. The more commonalities between the targets, the better the multi-target model that can be built. We show an example of a multi-target neural network that can use family information to produce a predictive model of an undersampled target. We evaluate JRank, a kernel-based method designed for collaborative filtering. We show their performance on compound prioritization for an HTS campaign and the underlying shared representation between targets. JRank outperformed the neural network both in the single- and multi-target models.
引用
收藏
页码:626 / 635
页数:10
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