Title
Matrix factorization-based data fusion for gene function prediction in baker's yeast and slime mold.
Abstract
The development of effective methods for the characterization of gene functions that are able to combine diverse data sources in a sound and easily-extendible way is an important goal in computational biology. We have previously developed a general matrix factorization-based data fusion approach for gene function prediction. In this manuscript, we show that this data fusion approach can be applied to gene function prediction and that it can fuse various heterogeneous data sources, such as gene expression pro files, known protein annotations, interaction and literature data. The fusion is achieved by simultaneous matrix tri-factorization that shares matrix factors between sources. We demonstrate the effectiveness of the approach by evaluating its performance on predicting ontological annotations in slime mold D. discoideum and on recognizing proteins of baker's yeast S. cerevisiae that participate in the ribosome or are located in the cell membrane. Our approach achieves predictive performance comparable to that of the state-of-the-art kernel-based data fusion, but requires fewer data preprocessing steps.
Year
Venue
Keywords
2014
Biocomputing-Pacific Symposium on Biocomputing
gene function prediction,data fusion,matrix factorization,Gene Ontology annotation,membrane protein,ribosomal protein
Field
DocType
ISSN
Kernel (linear algebra),Biology,Matrix (mathematics),Matrix decomposition,Data pre-processing,Sensor fusion,Protein Annotation,Slime mold,Bioinformatics,Molecular Sequence Annotation
Conference
2335-6936
Citations 
PageRank 
References 
10
0.54
0
Authors
2
Name
Order
Citations
PageRank
Marinka Zitnik134427.10
Blaz Zupan2161.80