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High-throughput experimental techniques have produced a large amount of human protein-protein interactions, making it possible to construct a large-scale human PPI network and detect human protein complexes from the network with computational approaches. However, most of current complex detection methods are based on graph theory which can't utilize the information of the known complexes. In this...
The rapidly growing biomedical literature provides a significantly large and readily available source of PPI data. In this paper, we present supervised learning and data integration based complex detection approach. In this approach, a sophisticated natural language processing system, PPIExtractor, is employed to extract new PPI interactions from biomedical literature which are then integrated into...
In this paper, we present a supervised learning-based method for predicting protein complexes in protein interaction network. The method extracts rich features from protein interaction network to train a Regression model, which is then used for the cliques filtering, growth, and candidate complex filtering. The experimental results on several protein interaction networks show that our method outperforms...
Feature representation is essential to machine learning and text mining. In this paper, we present a feature coupling generalization (FCG) framework for generating new features from unlabeled data. It selects two special types of features, i.e., example-distinguishing features (EDFs) and class-distinguishing features (CDFs) from original feature set, and then generalizes EDFs into higher-level features...
Nowadays, protein-protein interaction (PPI) extraction has become a research focus. Many methods have been applied to this domain, such as supervised learning approaches. This paper applied support vector machine (SVM) to extract PPI, which bases on several lexical features and one syntactic feature achieved through link grammar parser. Due to syntax's complexity different sentence structure can not...
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