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Protein–Protein Interaction Extraction Based on Improved All-Paths Kernel

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Extracting protein–protein interactions from biomedical literatures is an important task in biomedical text mining. In this paper, we propose an improved all-paths kernel based method for this task. First, we improve all-paths kernel method by adding adjacent label to node label so that it can match the contiguous label sequences of graphs. Second, we divide the dependency graph into the token subgraph and relation subgraph which can better represent sentence structure to graph kernel and efficiently reduce the weak features in feature space. We evaluate the proposed method on five publicly available PPI corpora and perform detailed comparisons with other methods. As a result, our method significantly outperforms all-paths kernel method.
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Document Type: Research Article

Publication date: October 1, 2011

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  • Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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