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Functional Annotation of Genes Using Hierarchical Text Categorization Svetlana Kiritchenko, Stan Matwin University of Ottawa, Canada and A. Fazel Famili.

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Presentation on theme: "Functional Annotation of Genes Using Hierarchical Text Categorization Svetlana Kiritchenko, Stan Matwin University of Ottawa, Canada and A. Fazel Famili."— Presentation transcript:

1 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

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 (d j, c i )  True, then (d j, Ancestor(c i ))  True c1c1 c7c7 c6c6 c5c5 c4c4 c3c3 c2c2 c1c1 c7c7 c6c6 c5c5 c4c4 c3c3 c2c2 consistentinconsistent

6 Hierarchical Local Approach c1c1 c7c7 c6c6 c5c5 c4c4 c3c3 c2c2 c8c8 c9c9

7 c1c1 c7c7 c6c6 c5c5 c4c4 c3c3 c2c2 c8c8 c9c9

8 c1c1 c7c7 c6c6 c5c5 c4c4 c3c3 c2c2 c8c8 c9c9

9 c1c1 c7c7 c6c6 c5c5 c4c4 c3c3 c2c2 c8c8 c9c9

10 c1c1 c7c7 c6c6 c5c5 c4c4 c3c3 c2c2 c8c8 c9c9 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 datasetlevelbranchingFlatHier. LocalHier. Global biol. process125.4115.0659.2759.31 mol. function1010.298.7843.3638.17 cell. component86.4544.1872.0773.35

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