Polyadic regression and its application to chemogenomics

Ioakeim Perros, Fei Wang, Ping Zhang, Peter Walker, Richard Vuduc, Jyotishman Pathak, Jimeng Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 17th SIAM International Conference on Data Mining, SDM 2017
EditorsNitesh Chawla, Wei Wang
PublisherSociety for Industrial and Applied Mathematics Publications
Pages72-80
Number of pages9
ISBN (Electronic)9781611974874
DOIs
StatePublished - Jan 1 2017
Event17th SIAM International Conference on Data Mining, SDM 2017 - Houston, United States
Duration: Apr 27 2017Apr 29 2017

Publication series

NameProceedings of the 17th SIAM International Conference on Data Mining, SDM 2017

Other

Other17th SIAM International Conference on Data Mining, SDM 2017
CountryUnited States
CityHouston
Period4/27/174/29/17

Keywords

  • Chemogenomics
  • Polyadic prediction
  • Tensors

ASJC Scopus subject areas

  • Software
  • Computer Science Applications

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  • Cite this

    Perros, I., Wang, F., Zhang, P., Walker, P., Vuduc, R., Pathak, J., & Sun, J. (2017). Polyadic regression and its application to chemogenomics. In N. Chawla, & W. Wang (Eds.), Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017 (pp. 72-80). (Proceedings of the 17th SIAM International Conference on Data Mining, SDM 2017). Society for Industrial and Applied Mathematics Publications. https://doi.org/10.1137/1.9781611974973.9