The Experts below are selected from a list of 27978 Experts worldwide ranked by ideXlab platform
Luis M Rocha - One of the best experts on this subject based on the ideXlab platform.
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a Linear Classifier based on entity recognition tools and a statistical approach to method extraction in the protein protein interaction literature
BMC Bioinformatics, 2011Co-Authors: Analia Lourenco, Michael Conover, Andrew Wong, Azadeh Nematzadeh, Fengxia Pan, Hagit Shatkay, Luis M RochaAbstract:Correction to A. Lourenco, M. Conover, A. Wong, A. Nematzadeh, F. Pan, H. Shatkay, and L.M. Rocha."A Linear Classifier Based on Entity Recognition Tools and a Statistical Approach to Method Extraction in the Protein-Protein Interaction Literature". BMC Bioinformatics 2011, 12(Suppl 8):S12. doi:http://10.1186/1471-2105-12-S8-S12.
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a Linear Classifier based on entity recognition tools and a statistical approach to method extraction in the protein protein interaction literature
arXiv: Quantitative Methods, 2011Co-Authors: Analia Lourenco, Michael Conover, Andrew Wong, Azadeh Nematzadeh, Fengxia Pan, Hagit Shatkay, Luis M RochaAbstract:We participated, in the Article Classification and the Interaction Method subtasks (ACT and IMT, respectively) of the Protein-Protein Interaction task of the BioCreative III Challenge. For the ACT, we pursued an extensive testing of available Named Entity Recognition and dictionary tools, and used the most promising ones to extend our Variable Trigonometric Threshold Linear Classifier. For the IMT, we experimented with a primarily statistical approach, as opposed to employing a deeper natural language processing strategy. Finally, we also studied the benefits of integrating the method extraction approach that we have used for the IMT into the ACT pipeline. For the ACT, our Linear article Classifier leads to a ranking and classification performance significantly higher than all the reported submissions. For the IMT, our results are comparable to those of other systems, which took very different approaches. For the ACT, we show that the use of named entity recognition tools leads to a substantial improvement in the ranking and classification of articles relevant to protein-protein interaction. Thus, we show that our substantially expanded Linear Classifier is a very competitive Classifier in this domain. Moreover, this Classifier produces interpretable surfaces that can be understood as "rules" for human understanding of the classification. In terms of the IMT task, in contrast to other participants, our approach focused on identifying sentences that are likely to bear evidence for the application of a PPI detection method, rather than on classifying a document as relevant to a method. As BioCreative III did not perform an evaluation of the evidence provided by the system, we have conducted a separate assessment; the evaluators agree that our tool is indeed effective in detecting relevant evidence for PPI detection methods.
Hagit Shatkay - One of the best experts on this subject based on the ideXlab platform.
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a Linear Classifier based on entity recognition tools and a statistical approach to method extraction in the protein protein interaction literature
BMC Bioinformatics, 2011Co-Authors: Analia Lourenco, Michael Conover, Andrew Wong, Azadeh Nematzadeh, Fengxia Pan, Hagit Shatkay, Luis M RochaAbstract:Correction to A. Lourenco, M. Conover, A. Wong, A. Nematzadeh, F. Pan, H. Shatkay, and L.M. Rocha."A Linear Classifier Based on Entity Recognition Tools and a Statistical Approach to Method Extraction in the Protein-Protein Interaction Literature". BMC Bioinformatics 2011, 12(Suppl 8):S12. doi:http://10.1186/1471-2105-12-S8-S12.
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a Linear Classifier based on entity recognition tools and a statistical approach to method extraction in the protein protein interaction literature
arXiv: Quantitative Methods, 2011Co-Authors: Analia Lourenco, Michael Conover, Andrew Wong, Azadeh Nematzadeh, Fengxia Pan, Hagit Shatkay, Luis M RochaAbstract:We participated, in the Article Classification and the Interaction Method subtasks (ACT and IMT, respectively) of the Protein-Protein Interaction task of the BioCreative III Challenge. For the ACT, we pursued an extensive testing of available Named Entity Recognition and dictionary tools, and used the most promising ones to extend our Variable Trigonometric Threshold Linear Classifier. For the IMT, we experimented with a primarily statistical approach, as opposed to employing a deeper natural language processing strategy. Finally, we also studied the benefits of integrating the method extraction approach that we have used for the IMT into the ACT pipeline. For the ACT, our Linear article Classifier leads to a ranking and classification performance significantly higher than all the reported submissions. For the IMT, our results are comparable to those of other systems, which took very different approaches. For the ACT, we show that the use of named entity recognition tools leads to a substantial improvement in the ranking and classification of articles relevant to protein-protein interaction. Thus, we show that our substantially expanded Linear Classifier is a very competitive Classifier in this domain. Moreover, this Classifier produces interpretable surfaces that can be understood as "rules" for human understanding of the classification. In terms of the IMT task, in contrast to other participants, our approach focused on identifying sentences that are likely to bear evidence for the application of a PPI detection method, rather than on classifying a document as relevant to a method. As BioCreative III did not perform an evaluation of the evidence provided by the system, we have conducted a separate assessment; the evaluators agree that our tool is indeed effective in detecting relevant evidence for PPI detection methods.
Michael Conover - One of the best experts on this subject based on the ideXlab platform.
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a Linear Classifier based on entity recognition tools and a statistical approach to method extraction in the protein protein interaction literature
BMC Bioinformatics, 2011Co-Authors: Analia Lourenco, Michael Conover, Andrew Wong, Azadeh Nematzadeh, Fengxia Pan, Hagit Shatkay, Luis M RochaAbstract:Correction to A. Lourenco, M. Conover, A. Wong, A. Nematzadeh, F. Pan, H. Shatkay, and L.M. Rocha."A Linear Classifier Based on Entity Recognition Tools and a Statistical Approach to Method Extraction in the Protein-Protein Interaction Literature". BMC Bioinformatics 2011, 12(Suppl 8):S12. doi:http://10.1186/1471-2105-12-S8-S12.
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a Linear Classifier based on entity recognition tools and a statistical approach to method extraction in the protein protein interaction literature
arXiv: Quantitative Methods, 2011Co-Authors: Analia Lourenco, Michael Conover, Andrew Wong, Azadeh Nematzadeh, Fengxia Pan, Hagit Shatkay, Luis M RochaAbstract:We participated, in the Article Classification and the Interaction Method subtasks (ACT and IMT, respectively) of the Protein-Protein Interaction task of the BioCreative III Challenge. For the ACT, we pursued an extensive testing of available Named Entity Recognition and dictionary tools, and used the most promising ones to extend our Variable Trigonometric Threshold Linear Classifier. For the IMT, we experimented with a primarily statistical approach, as opposed to employing a deeper natural language processing strategy. Finally, we also studied the benefits of integrating the method extraction approach that we have used for the IMT into the ACT pipeline. For the ACT, our Linear article Classifier leads to a ranking and classification performance significantly higher than all the reported submissions. For the IMT, our results are comparable to those of other systems, which took very different approaches. For the ACT, we show that the use of named entity recognition tools leads to a substantial improvement in the ranking and classification of articles relevant to protein-protein interaction. Thus, we show that our substantially expanded Linear Classifier is a very competitive Classifier in this domain. Moreover, this Classifier produces interpretable surfaces that can be understood as "rules" for human understanding of the classification. In terms of the IMT task, in contrast to other participants, our approach focused on identifying sentences that are likely to bear evidence for the application of a PPI detection method, rather than on classifying a document as relevant to a method. As BioCreative III did not perform an evaluation of the evidence provided by the system, we have conducted a separate assessment; the evaluators agree that our tool is indeed effective in detecting relevant evidence for PPI detection methods.
Azadeh Nematzadeh - One of the best experts on this subject based on the ideXlab platform.
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a Linear Classifier based on entity recognition tools and a statistical approach to method extraction in the protein protein interaction literature
BMC Bioinformatics, 2011Co-Authors: Analia Lourenco, Michael Conover, Andrew Wong, Azadeh Nematzadeh, Fengxia Pan, Hagit Shatkay, Luis M RochaAbstract:Correction to A. Lourenco, M. Conover, A. Wong, A. Nematzadeh, F. Pan, H. Shatkay, and L.M. Rocha."A Linear Classifier Based on Entity Recognition Tools and a Statistical Approach to Method Extraction in the Protein-Protein Interaction Literature". BMC Bioinformatics 2011, 12(Suppl 8):S12. doi:http://10.1186/1471-2105-12-S8-S12.
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a Linear Classifier based on entity recognition tools and a statistical approach to method extraction in the protein protein interaction literature
arXiv: Quantitative Methods, 2011Co-Authors: Analia Lourenco, Michael Conover, Andrew Wong, Azadeh Nematzadeh, Fengxia Pan, Hagit Shatkay, Luis M RochaAbstract:We participated, in the Article Classification and the Interaction Method subtasks (ACT and IMT, respectively) of the Protein-Protein Interaction task of the BioCreative III Challenge. For the ACT, we pursued an extensive testing of available Named Entity Recognition and dictionary tools, and used the most promising ones to extend our Variable Trigonometric Threshold Linear Classifier. For the IMT, we experimented with a primarily statistical approach, as opposed to employing a deeper natural language processing strategy. Finally, we also studied the benefits of integrating the method extraction approach that we have used for the IMT into the ACT pipeline. For the ACT, our Linear article Classifier leads to a ranking and classification performance significantly higher than all the reported submissions. For the IMT, our results are comparable to those of other systems, which took very different approaches. For the ACT, we show that the use of named entity recognition tools leads to a substantial improvement in the ranking and classification of articles relevant to protein-protein interaction. Thus, we show that our substantially expanded Linear Classifier is a very competitive Classifier in this domain. Moreover, this Classifier produces interpretable surfaces that can be understood as "rules" for human understanding of the classification. In terms of the IMT task, in contrast to other participants, our approach focused on identifying sentences that are likely to bear evidence for the application of a PPI detection method, rather than on classifying a document as relevant to a method. As BioCreative III did not perform an evaluation of the evidence provided by the system, we have conducted a separate assessment; the evaluators agree that our tool is indeed effective in detecting relevant evidence for PPI detection methods.
Analia Lourenco - One of the best experts on this subject based on the ideXlab platform.
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a Linear Classifier based on entity recognition tools and a statistical approach to method extraction in the protein protein interaction literature
BMC Bioinformatics, 2011Co-Authors: Analia Lourenco, Michael Conover, Andrew Wong, Azadeh Nematzadeh, Fengxia Pan, Hagit Shatkay, Luis M RochaAbstract:Correction to A. Lourenco, M. Conover, A. Wong, A. Nematzadeh, F. Pan, H. Shatkay, and L.M. Rocha."A Linear Classifier Based on Entity Recognition Tools and a Statistical Approach to Method Extraction in the Protein-Protein Interaction Literature". BMC Bioinformatics 2011, 12(Suppl 8):S12. doi:http://10.1186/1471-2105-12-S8-S12.
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a Linear Classifier based on entity recognition tools and a statistical approach to method extraction in the protein protein interaction literature
arXiv: Quantitative Methods, 2011Co-Authors: Analia Lourenco, Michael Conover, Andrew Wong, Azadeh Nematzadeh, Fengxia Pan, Hagit Shatkay, Luis M RochaAbstract:We participated, in the Article Classification and the Interaction Method subtasks (ACT and IMT, respectively) of the Protein-Protein Interaction task of the BioCreative III Challenge. For the ACT, we pursued an extensive testing of available Named Entity Recognition and dictionary tools, and used the most promising ones to extend our Variable Trigonometric Threshold Linear Classifier. For the IMT, we experimented with a primarily statistical approach, as opposed to employing a deeper natural language processing strategy. Finally, we also studied the benefits of integrating the method extraction approach that we have used for the IMT into the ACT pipeline. For the ACT, our Linear article Classifier leads to a ranking and classification performance significantly higher than all the reported submissions. For the IMT, our results are comparable to those of other systems, which took very different approaches. For the ACT, we show that the use of named entity recognition tools leads to a substantial improvement in the ranking and classification of articles relevant to protein-protein interaction. Thus, we show that our substantially expanded Linear Classifier is a very competitive Classifier in this domain. Moreover, this Classifier produces interpretable surfaces that can be understood as "rules" for human understanding of the classification. In terms of the IMT task, in contrast to other participants, our approach focused on identifying sentences that are likely to bear evidence for the application of a PPI detection method, rather than on classifying a document as relevant to a method. As BioCreative III did not perform an evaluation of the evidence provided by the system, we have conducted a separate assessment; the evaluators agree that our tool is indeed effective in detecting relevant evidence for PPI detection methods.
