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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)
BACKGROUND Most proteins form macromolecular complexes to perform their biological functions. However, experimentally determined protein complex data, especially of those involving more than two protein partners, are relatively limited in the current state-of-the-art high-throughput experimental techniques. Nevertheless, many techniques (such as(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)
While recent technological advances have made available large datasets of experimentally-detected pairwise protein-protein interactions, there is still a lack of experimentally-determined protein complex data. To make up for this lack of protein complex data, we explore the mining of existing protein interaction graphs for protein complexes. This paper(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)