Cancer module genes ranking using kernelized score functions

Object proposal with kernelized partial ranking

Object proposals are an ensemble of bounding boxes with high potential to contain objects. In order to determine a small set of proposals with a high recall, a common scheme is extracting multiple features followed by a ranking algorithm which however, incurs two major challenges: 1) The ranking model often imposes pairwise constraints between each proposal, rendering the problem away from an e...

Kernelized Subspace Ranking for Saliency Detection

An enhanced XCS rule discovery module using feature ranking

XCS is a genetics-based machine learning model that combines reinforcement learning with evolutionary algorithms to evolve a population of classifiers in the form of condition-action rules. Like many other machine learning algorithms, XCS is less effective on high-dimensional data sets. In this paper, we describe a new guided rule discovery mechanisms for XCS, inspired by feature selection tech...

Ranking, selecting, and prioritising genes with desirability functions

In functional genomics experiments, researchers often select genes to follow-up or validate from a long list of differentially expressed genes. Typically, sharp thresholds are used to bin genes into groups such as significant/non-significant or fold change above/below a cut-off value, and ad hoc criteria are also used such as favouring well-known genes. Binning, however, is inefficient and does...

Ranking hubs and authorities using matrix functions

The notions of subgraph centrality and communicability, based on the exponential of the adjacency matrix of the underlying graph, have been effectively used in the analysis of undirected networks. In this paper we propose an extension of these measures to directed networks, and we apply them to the problem of ranking hubs and authorities. The extension is achieved by bipartization, i.e., the di...