The graph classification problem consists, given a weighted graph and a partial node labeling, in extending the labels to all nodes. In many real-world context, such as Gene Function Prediction, the partial labeling is unbalanced: positive labels are much less than negatives. In this paper we present a new neural algorithm for predicting labels in presence of label imbalance. This algorithm is based on a family of Hopfield networks, described by 2 continuous parameters and 1 discrete parameter, and it consists of two main steps: 1) the network parameters are learnt through a cost-sensitive optimization procedure based on local search; 2) a suitable Hopfield network restricted to unlabeled nodes is considered and simulated. The reached equilibrium point induces the classification of unlabeled nodes. An experimental analysis on real-world unbalanced data in the context of genome-wide prediction of gene functions show the effectiveness of the proposed approach.
|Titolo:||A Neural Procedure for Gene Function Prediction|
|Autori interni:||FRASCA, MARCO (Primo)|
BERTONI, ALBERTO (Secondo)
|Parole Chiave:||Gene function prediction; Hopfield network; Neural network|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
|Data di pubblicazione:||2013|
|Digital Object Identifier (DOI):||10.1007/978-3-642-35467-0_19|
|Tipologia:||Book Part (author)|
|Appare nelle tipologie:||03 - Contributo in volume|
File in questo prodotto:
- PubMed Central loading...