Metric learning pairwise kernel for graph inference
<|reference_start|>Metric learning pairwise kernel for graph inference: 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.). 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 (MLPK). We demonstrate, using several real biological networks, that this direct approach often improves upon the state-of-the-art SVM for indirect inference with the tensor product pairwise kernel.<|reference_end|>
@article{vert2006metric,
    title={Metric learning pairwise kernel for graph inference},
    author={Jean-Philippe Vert (CB), Jian Qiu (GS-UW), William Stafford Noble
  (GS-UW)},
    journal={arXiv preprint arXiv:q-bio/0610040},
    year={2006},
    archivePrefix={arXiv},
    eprint={q-bio/0610040},
    primaryClass={q-bio.QM cs.LG}
}