Functional Annotation of Genes Using Hierarchical

Text Categorization

Svetlana Kiritchenko, Stan Matwin University of Ottawa, Canada

andA. Fazel Famili

National Research Council of Canada

Functional Annotation of Genes from Biomedical

Literature

Previous Research

• Raychaudhuri et al. (2002)

• BioCreative workshop (2004)

• No hierarchical information has been used

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

Hierarchical consistency

• if (dj, ci) True,

then (dj, Ancestor(ci)) True

c1

c7c6c5c4

c3c2

c1

c7c6c5c4

c3c2

consistent inconsistent

Hierarchical Local Approach

c1

c7c6c5c4

c3c2

c8 c9

Hierarchical Local Approach

c1

c7c6c5c4

c3c2

c8 c9

Hierarchical Local Approach

c1

c7c6c5c4

c3c2

c8 c9

Hierarchical Local Approach

c1

c7c6c5c4

c3c2

c8 c9

Hierarchical Local Approach

c1

c7c6c5c4

c3c2

c8 c9

consistent classification

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

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

Results

dataset level branching Flat Hier. Local Hier. Global

biol. process 12 5.41 15.06 59.27 59.31

mol. function 10 10.29 8.78 43.36 38.17

cell. component 8 6.45 44.18 72.07 73.35