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This paper studies the problem of building text classifiers using positive and unlabeled examples. The key feature of this problem is that there is no negative example for learning. Recently, a few techniques for solving this problem were proposed in the literature. These techniques are based on the same idea, which builds a classifier in two steps. Each(More)
We investigate the following problem: Given a set of documents of a particular topic or class È , and a large set Å of mixed documents that contains documents from class È and other types of documents, identify the documents from class È in Å. The key feature of this problem is that there is no labeled non-È document, which makes traditional machine(More)
BACKGROUND How to detect protein complexes is an important and challenging task in post genomic era. As the increasing amount of protein-protein interaction (PPI) data are available, we are able to identify protein complexes from PPI networks. However, most of current studies detect protein complexes based solely on the observation that dense regions in PPI(More)
MOTIVATION In silico methods provide efficient ways to predict possible interactions between drugs and targets. Supervised learning approach, bipartite local model (BLM), has recently been shown to be effective in prediction of drug-target interactions. However, for drug-candidate compounds or target-candidate proteins that currently have no known(More)
This paper describes the use of a computational tool based on the Morlet wavelet transform to investigate the interaction dynamics between oscillations generated by two anatomically distinct neuronal populations. The tool uses cross wavelet transform, coherence, bi-spectrum/bi-coherence and phase synchronization. Using specimen data recorded from the(More)
BACKGROUND Approximate entropy (AE) has been proposed as a measure of anesthetic drug effect in electroencephalographic data. Recently, a new method called permutation entropy (PE) based on symbolic dynamics was also proposed to measure the complexity in an electroencephalographic series. In this study, the AE and PE were applied to electroencephalographic(More)
The purpose of the present paper is to develop a method, based on equal-time correlation, correlation matrix analysis and surrogate resampling, that is able to quantify and describe properties of synchronization of population neuronal activity recorded simultaneously from multiple sites. Initially, Lorenz-type oscillators were used to model multiple time(More)
OBJECTIVE Ordinal patterns analysis such as permutation entropy of the EEG series has been found to usefully track brain dynamics and has been applied to detect changes in the dynamics of EEG data. In order to further investigate hidden nonlinear dynamical characteristics in EEG data for differentiating brain states, this paper proposes a novel(More)