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Chemical-induced disease relations (CID) are crucial in various biomedical tasks. In the CID task of Biocreative V, no classifiers with multiple kernels have been developed. In this study, a multiple… Click to show full abstract
Chemical-induced disease relations (CID) are crucial in various biomedical tasks. In the CID task of Biocreative V, no classifiers with multiple kernels have been developed. In this study, a multiple kernel learning-boosting (MKLB) method is proposed. Different kernel functions according to different types of features were constucted and boosted, each of which were learned with multiple kernels. Our multiple kernel learning-boosting (MKLB) method achieved a F1 score of 0.5068 without incorporating knowledge bases.
Keywords:
learning boosting;
multiple kernel;
identifying chemical;
disease;
method;
kernel learning
Journal Title: Studies in health technology and informatics
Year Published: 2017
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Journal Title: Studies in health technology and informatics
Year Published: 2017
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!