A new pairwise kernel for biological network inference with support vector machines.
BACKGROUND
Much recent work in bioinformatics has focused on the inference of various types of biological networks, representing gene regulation, metabolic processes, protein-protein interactions, etc. A common setting involves inferring network edges in a supervised fashion from a set of high-confidence edges, possibly characterized by multiple, heterogeneous data sets (protein sequence, gene expression, etc.).


RESULTS
Here, we distinguish between two modes of inference in this setting: direct inference based upon similarities between nodes joined by an edge, and indirect inference based upon similarities between one pair of nodes and another pair of nodes. We propose a supervised approach for the direct case by translating it into a distance metric learning problem. A relaxation of the resulting convex optimization problem leads to the support vector machine (SVM) algorithm with a particular kernel for pairs, which we call the metric learning pairwise kernel. This new kernel for pairs can easily be used by most SVM implementations to solve problems of supervised classification and inference of pairwise relationships from heterogeneous data. We demonstrate, using several real biological networks and genomic datasets, that this approach often improves upon the state-of-the-art SVM for indirect inference with another pairwise kernel, and that the combination of both kernels always improves upon each individual kernel.


CONCLUSIONS
The metric learning pairwise kernel is a new formulation to infer pairwise relationships with SVM, which provides state-of-the-art results for the inference of several biological networks from heterogeneous genomic data.
Vert
Jean-Philippe
JP
Centre for Computational Biology, Ecole des Mines de Paris, 35 rue Saint-Honoré, 77300 Fontainebleau, France. Jean-Philippe.Vert@ensmp.fr
Qiu
Jian
J
Noble
William S
WS
D000465:Algorithms; D020869:Gene Expression Profiling; D053858:Metabolic Networks and Pathways / Q000502:physiology; D015233:Models, Statistical; D012441:Saccharomyces cerevisiae / Q000737:chemistry / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism; D015398:Signal Transduction / Q000235:genetics; D049490:Systems Biology / Q000379:methods* / Q000706:statistics & numerical data
D029701:Saccharomyces cerevisiae Proteins