Background: Recently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. The reconstruction of a network is modeled...
The input to an algorithm that learns a binary classifier normally consists of two sets of examples, where one set consists of positive examples of the concept to be learned, and ...
Abstract. The problem of classification from positive and unlabeled examples attracts much attention currently. However, when the number of unlabeled negative examples is very sma...
Xiaoling Wang, Zhen Xu, Chaofeng Sha, Martin Ester...
Exploring gene regulatory network is a key topic in molecular biology. In this paper, we present a new dynamic Bayesian network (DBN) framework embedded with structural expectatio...
Background: Gene regulatory networks play essential roles in living organisms to control growth, keep internal metabolism running and respond to external environmental changes. Un...