Data Fusion by Matrix Factorization
This work addresses data integration challenges in fields like bioinformatics and pharmacology, though it is incremental as it builds on existing matrix factorization techniques.
The paper tackled the problem of integrating heterogeneous data sources by developing a data fusion approach using penalized matrix tri-factorization (DFMF) to reveal hidden associations. It demonstrated higher accuracy in gene function prediction and pharmacologic action prediction compared to single data sources and alternative integration methods.
For most problems in science and engineering we can obtain data sets that describe the observed system from various perspectives and record the behavior of its individual components. Heterogeneous data sets can be collectively mined by data fusion. Fusion can focus on a specific target relation and exploit directly associated data together with contextual data and data about system's constraints. In the paper we describe a data fusion approach with penalized matrix tri-factorization (DFMF) that simultaneously factorizes data matrices to reveal hidden associations. The approach can directly consider any data that can be expressed in a matrix, including those from feature-based representations, ontologies, associations and networks. We demonstrate the utility of DFMF for gene function prediction task with eleven different data sources and for prediction of pharmacologic actions by fusing six data sources. Our data fusion algorithm compares favorably to alternative data integration approaches and achieves higher accuracy than can be obtained from any single data source alone.