Marinka Zitnik

Fusing bits and DNA

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Bioinformatics: Jumping Across Contexts Using Compressive Fusion

Our paper on Jumping across biomedical contexts using compressive data fusion has just appeared in Bioinformatics. We will present the paper at ISMB 2016 in July 2016.

The rapid growth of diverse biological data allows us to consider interactions between a variety of objects, such as genes, chemicals, molecular signatures, diseases, pathways and environmental exposures. Often, any pair of objects—such as a gene and a disease—can be related in different ways, for example, directly via gene–disease associations or indirectly via functional annotations, chemicals and pathways. In this paper, we show that different ways of relating these objects carry different semantic meanings that are largely ignored by established computational methods.

We present an approach that operates on large-scale heterogeneous data collections and explicitly distinguishes between diverse data semantics. The approach detects size-k modules of objects that, taken together, appear most significant to another set of objects. The method builds on collective matrix factorization to derive different semantics, and it formulates the growing of the modules as a submodular optimization program.

In a systematic study on more than three hundred complex diseases, we show the effectiveness of the approach in associating genes with diseases and detecting disease modules.