The Experts below are selected from a list of 192 Experts worldwide ranked by ideXlab platform
Hongfei Lin - One of the best experts on this subject based on the ideXlab platform.
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Ranking support vector machine for multiple kernels Output Combination in protein-protein interaction extraction from biomedical literature.
Proteomics, 2011Co-Authors: Zhihao Yang, Yuan Lin, Nan Tang, Hongfei LinAbstract:Knowledge about protein-protein interactions (PPIs) unveils the molecular mechanisms of biological processes. However, the volume and content of published biomedical literature on protein interactions is expanding rapidly, making it increasingly difficult for interaction database curators to detect and curate protein interaction information manually. We present a multiple kernel learning-based approach for automatic PPI extraction from biomedical literature. The approach combines the following kernels: feature-based, tree, and graph and combines their Output with Ranking support vector machine (SVM). Experimental evaluations show that the features in individual kernels are complementary and the kernel combined with Ranking SVM achieves better performance than those of the individual kernels, equal weight Combination and optimal weight Combination. Our approach can achieve state-of-the-art performance with respect to the comparable evaluations, with 64.88% F-score and 88.02% AUC on the AImed corpus.
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BIBM - Ranking SVM for multiple kernels Output Combination in protein-protein interaction extraction from biomedical literature
2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2010Co-Authors: Zhihao Yang, Yuan Lin, Nan Tang, Hongfei LinAbstract:Knowledge about protein-protein interactions unveils the molecular mechanisms of biological processes. This paper presents a multiple kernels learning-based approach to automatically extracting protein-protein interactions from biomedical literature. Experimental evaluations show that our approach can achieve state-of-the-art performance with respect to comparable evaluations, with 64.88% F-score and 88.02% area under the receiver operating characteristics curve (AUC) on the AImed corpus.
Zhihao Yang - One of the best experts on this subject based on the ideXlab platform.
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Ranking support vector machine for multiple kernels Output Combination in protein-protein interaction extraction from biomedical literature.
Proteomics, 2011Co-Authors: Zhihao Yang, Yuan Lin, Nan Tang, Hongfei LinAbstract:Knowledge about protein-protein interactions (PPIs) unveils the molecular mechanisms of biological processes. However, the volume and content of published biomedical literature on protein interactions is expanding rapidly, making it increasingly difficult for interaction database curators to detect and curate protein interaction information manually. We present a multiple kernel learning-based approach for automatic PPI extraction from biomedical literature. The approach combines the following kernels: feature-based, tree, and graph and combines their Output with Ranking support vector machine (SVM). Experimental evaluations show that the features in individual kernels are complementary and the kernel combined with Ranking SVM achieves better performance than those of the individual kernels, equal weight Combination and optimal weight Combination. Our approach can achieve state-of-the-art performance with respect to the comparable evaluations, with 64.88% F-score and 88.02% AUC on the AImed corpus.
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BIBM - Ranking SVM for multiple kernels Output Combination in protein-protein interaction extraction from biomedical literature
2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2010Co-Authors: Zhihao Yang, Yuan Lin, Nan Tang, Hongfei LinAbstract:Knowledge about protein-protein interactions unveils the molecular mechanisms of biological processes. This paper presents a multiple kernels learning-based approach to automatically extracting protein-protein interactions from biomedical literature. Experimental evaluations show that our approach can achieve state-of-the-art performance with respect to comparable evaluations, with 64.88% F-score and 88.02% area under the receiver operating characteristics curve (AUC) on the AImed corpus.
Yuan Lin - One of the best experts on this subject based on the ideXlab platform.
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Ranking support vector machine for multiple kernels Output Combination in protein-protein interaction extraction from biomedical literature.
Proteomics, 2011Co-Authors: Zhihao Yang, Yuan Lin, Nan Tang, Hongfei LinAbstract:Knowledge about protein-protein interactions (PPIs) unveils the molecular mechanisms of biological processes. However, the volume and content of published biomedical literature on protein interactions is expanding rapidly, making it increasingly difficult for interaction database curators to detect and curate protein interaction information manually. We present a multiple kernel learning-based approach for automatic PPI extraction from biomedical literature. The approach combines the following kernels: feature-based, tree, and graph and combines their Output with Ranking support vector machine (SVM). Experimental evaluations show that the features in individual kernels are complementary and the kernel combined with Ranking SVM achieves better performance than those of the individual kernels, equal weight Combination and optimal weight Combination. Our approach can achieve state-of-the-art performance with respect to the comparable evaluations, with 64.88% F-score and 88.02% AUC on the AImed corpus.
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BIBM - Ranking SVM for multiple kernels Output Combination in protein-protein interaction extraction from biomedical literature
2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2010Co-Authors: Zhihao Yang, Yuan Lin, Nan Tang, Hongfei LinAbstract:Knowledge about protein-protein interactions unveils the molecular mechanisms of biological processes. This paper presents a multiple kernels learning-based approach to automatically extracting protein-protein interactions from biomedical literature. Experimental evaluations show that our approach can achieve state-of-the-art performance with respect to comparable evaluations, with 64.88% F-score and 88.02% area under the receiver operating characteristics curve (AUC) on the AImed corpus.
Nan Tang - One of the best experts on this subject based on the ideXlab platform.
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Ranking support vector machine for multiple kernels Output Combination in protein-protein interaction extraction from biomedical literature.
Proteomics, 2011Co-Authors: Zhihao Yang, Yuan Lin, Nan Tang, Hongfei LinAbstract:Knowledge about protein-protein interactions (PPIs) unveils the molecular mechanisms of biological processes. However, the volume and content of published biomedical literature on protein interactions is expanding rapidly, making it increasingly difficult for interaction database curators to detect and curate protein interaction information manually. We present a multiple kernel learning-based approach for automatic PPI extraction from biomedical literature. The approach combines the following kernels: feature-based, tree, and graph and combines their Output with Ranking support vector machine (SVM). Experimental evaluations show that the features in individual kernels are complementary and the kernel combined with Ranking SVM achieves better performance than those of the individual kernels, equal weight Combination and optimal weight Combination. Our approach can achieve state-of-the-art performance with respect to the comparable evaluations, with 64.88% F-score and 88.02% AUC on the AImed corpus.
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BIBM - Ranking SVM for multiple kernels Output Combination in protein-protein interaction extraction from biomedical literature
2010 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2010Co-Authors: Zhihao Yang, Yuan Lin, Nan Tang, Hongfei LinAbstract:Knowledge about protein-protein interactions unveils the molecular mechanisms of biological processes. This paper presents a multiple kernels learning-based approach to automatically extracting protein-protein interactions from biomedical literature. Experimental evaluations show that our approach can achieve state-of-the-art performance with respect to comparable evaluations, with 64.88% F-score and 88.02% area under the receiver operating characteristics curve (AUC) on the AImed corpus.
Christopher Marshall - One of the best experts on this subject based on the ideXlab platform.
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Combined Statistical, Biological and Categorical Models for Sensor Fusion
2010Co-Authors: James Bonick, Christopher MarshallAbstract:Abstract : The USA RDECOM CERDEC Night Vision and Electronic Sensors Directorate's Science and Technology Division investigated sensor fusion along three avenues: statistical, biological and categorical. The first two approaches are analyzed simultaneously to provide a precise and rigorous sensor fusion methodology. The statistical model currently enhances Bayesian methods for tracking, and suggests further application to target identification and fusion-involving both low level feature extraction and higher level sensor Output Combination. The biological model is also applied to multiple levels of the fusion problem. On the lowest level, it utilizes biologically-inspired results for improved feature extraction. On the higher levels, it develops biologically-inspired agency algorithms for sensor Output Combination and sensor network analysis. Ultimately, we model the entire fusion process with category theory. Category theory allows for the application of advanced mathematical theory to fusion analysis. In addition to using category theory as a modeling tool, in this paper we adapt categorical logic via topos theory to provide an advanced framework for decision fusion-initially using the topos of graphs.
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Statistical, biological, and categorical sensor fusion : an integrated methodology
Signal Processing Sensor Fusion and Target Recognition XVII, 2008Co-Authors: James Bonick, Christopher MarshallAbstract:In this paper, we investigate sensor fusion along three avenues: statistical, biological, and categorical. The first two approaches are analyzed simultaneously to provide a precise and rigorous sensor fusion methodology. The statistical model currently enhances Bayesian methods for tracking, and suggests further application to target identification and fusion - involving both low level feature extraction and higher level sensor Output Combination. The biological model is also applied to multiple levels of the fusion problem. On the lowest level, it utilizes biologically-inspired results for improved feature extraction. On the higher levels, it develops biologically-inspired evolutionary and agency algorithms for sensor Output Combination and sensor network analysis. Ultimately, we model the entire fusion process with category theory. Category theory allows for the application of advanced mathematical theory to fusion analysis. In addition to using category theory as a modeling tool, in this paper we adapt categorical logic via topos theory to provide an advanced framework for decision fusion - initially using the topos of graphs. Graphs are a simpler representation. We suggest formulations which will be richer - toward the goal of a theoretically robust and computationally practical sensor fusion system for assisted/automatic target recognition.
