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Functional Annotation of Genes Using Hierarchical Text Categorization

Functional Annotation of Genes Using Hierarchical Text Categorization. Svetlana Kiritchenko, Stan Matwin University of Ottawa, Canada and A. Fazel Famili National Research Council of Canada. Functional Annotation of Genes from Biomedical Literature. Previous Research.

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Functional Annotation of Genes Using Hierarchical Text Categorization

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  1. Functional Annotation of Genes Using Hierarchical Text Categorization Svetlana Kiritchenko, Stan MatwinUniversity of Ottawa, Canada and A. Fazel Famili National Research Council of Canada

  2. Functional Annotation of Genes from Biomedical Literature

  3. Previous Research • Raychaudhuri et al. (2002) • BioCreative workshop (2004) • No hierarchical information has been used

  4. Advantages of Hierarchical Approach • Additional, potentially valuable information • Relationships between categories • Flexibility • High levels: general topics • Low levels: more detail • Hierarchical evaluation • Give credit to partially correct classification

  5. Hierarchical consistency • if (dj, ci) True, then (dj, Ancestor(ci)) True c1 c1 c2 c2 c3 c3 c5 c5 c4 c6 c7 c4 c6 c7 consistent inconsistent

  6. Hierarchical Local Approach c1 c2 c3 c5 c4 c6 c7 c8 c9

  7. Hierarchical Local Approach c1 c2 c3 c5 c4 c6 c7 c8 c9

  8. Hierarchical Local Approach c1 c2 c3 c5 c4 c6 c7 c8 c9

  9. Hierarchical Local Approach c1 c2 c3 c5 c4 c6 c7 c8 c9

  10. Hierarchical Local Approach c1 c2 c3 c5 c4 c6 c7 c8 c9 consistent classification

  11. New Global Hierarchical Approach • Make a dataset consistent with a class hierarchy • add ancestor category labels • Apply a regular learning algorithm • AdaBoost • Make prediction results consistent with a class hierarchy • for inconsistent labeling make a consistent decision based on confidences of all ancestor classes

  12. New Hierarchical Evaluation Measure • Precision/Recall considering all ancestors of a correct (predicted) category • Simple, straight-forward to calculate • Based solely on a given hierarchy (no parameters to tune) • Gives credit to partially correct classification • Discriminates by distance and depth • Allows to trade off between classification precision and classification depth

  13. Results

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