Metric learning pairwise kernel for graph inference
| dc.creator | Vert, Jean-Philippe | |
| dc.creator | Qiu, Jian | |
| dc.creator | Noble, William Stafford | |
| dc.date | 2006-10-21 | |
| dc.date.accessioned | 2026-07-07T07:30:11Z | |
| dc.date.available | 2026-07-07T07:30:11Z | |
| dc.description | 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. | |
| dc.identifier | https://arxiv.org/abs/q-bio/0610040 | |
| dc.identifier | http://arxiv.org/abs/q-bio/0610040 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/118379 | |
| dc.subject | Quantitative Methods | |
| dc.subject | Machine Learning | |
| dc.title | Metric learning pairwise kernel for graph inference | |
| dc.type | text |