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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
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Functional Annotation of Genes from Biomedical Literature
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Previous Research Raychaudhuri et al. (2002) BioCreative workshop (2004) No hierarchical information has been used
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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
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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
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Hierarchical Local Approach c1c1 c7c7 c6c6 c5c5 c4c4 c3c3 c2c2 c8c8 c9c9
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c1c1 c7c7 c6c6 c5c5 c4c4 c3c3 c2c2 c8c8 c9c9
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c1c1 c7c7 c6c6 c5c5 c4c4 c3c3 c2c2 c8c8 c9c9
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c1c1 c7c7 c6c6 c5c5 c4c4 c3c3 c2c2 c8c8 c9c9
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c1c1 c7c7 c6c6 c5c5 c4c4 c3c3 c2c2 c8c8 c9c9 consistent classification
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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
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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
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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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