Marinka Zitnik

Fusing bits and DNA

  • Increase font size
  • Default font size
  • Decrease font size
Home

PSB 2016: Collective Pairwise Classification for Multi-Way Analysis

Our paper on Collective Pairwise Classification for Multi-Way Analysis has been published in the Proceedings of the 21st Pacific Symposium on Biocomputing. We will present the work at the PSB conference in January 2016.

In the paper, we develop a collective pairwise classification approach for multi-way data analysis. The approach leverages the superiority of latent factor models for analyzing large heterogeneous relational data sets and provides probabilistic estimates of relationships by optimizing a pairwise ranking loss. Although the method bears correspondence with the maximization of a non-differentiable area under the receiver operating characteristic curve, we were able to design a learning algorithm that scales well on large multi-relational data.

We used the method to infer relationships from multiplex drug data and to predict connections between clinical manifestations of diseases and their underlying molecular signatures. An appealing property of the method is its ability to make category-jumping inferences, such as predictions about diseases based solely on genomic and clinical data generated far outside the molecular context.