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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Identifying Chemical-Disease Relationship in Biomedical Text Using a Multiple Kernel Learning-Boosting Method.

Yueping Sun1, Yu Zhang1, Jiao Li1

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|January 4, 2018
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A new multiple kernel learning-boosting method effectively identifies chemical-induced disease relations. This approach achieved a significant F1 score, advancing the field of biomedical text mining.

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Area of Science:

  • Biomedical informatics
  • Computational biology
  • Natural language processing

Background:

  • Chemical-induced disease relations (CID) are vital for biomedical research and drug discovery.
  • Previous computational approaches for CID lacked sophisticated kernel-based methods.

Purpose of the Study:

  • To propose and evaluate a novel multiple kernel learning-boosting (MKLB) method for identifying chemical-induced disease relations.
  • To address the gap in using multi-kernel classifiers for the CID task.

Main Methods:

  • Developed a multiple kernel learning-boosting (MKLB) framework.
  • Constructed and boosted diverse kernel functions tailored to different feature types.
  • Learned models using multiple kernels to capture complex relationships.

Main Results:

  • The proposed MKLB method achieved a notable F1 score of 0.5068.
  • Performance was demonstrated without the integration of external knowledge bases.
  • The study represents the first application of multi-kernel classifiers to the Biocreative V CID task.

Conclusions:

  • The MKLB method shows promise for enhancing the accuracy of chemical-induced disease relation extraction.
  • This approach offers a flexible and powerful framework for integrating heterogeneous features in biomedical text mining.
  • Further research can explore incorporating knowledge bases to potentially improve performance.