Answering Gene Ontology terms to proteomics questions by supervised macro reading in MEDLINE

1. Julien Gobeill1, Emilie Pasche2, Douglas Teodoro2, Anne-Lise Veuthey3, Patrick Ruch1 1 University of Applied Sciences, Information Sciences, Geneva 2 Hospitals and University of Geneva, Geneva 3 Swiss-Protgroup, Swiss Institute of Bioinformatics, Geneva Answering Gene Ontology terms to proteomics questions by supervisedmacro reading in MEDLINE

2. Data deluge… “Whatis the subcellular location of protein MEN1 ? ” “What molecular functions are affected by Ryanodine ?”

3. Ontology-based search engines

4. Question Answering (EAGLi system) Redundancy hypothesis: The number of associated/co-occurring answers dominate other dimensions

5. Best way for extracting GO terms from a set of abstracts ? (1/3) • Comparisonbased in twocategorizers : • Thesaurus-Based (EAGL) • CompetitivewithMetaMap(Trieschnigg et al., 2009) • Computelex. similaritybetweentext and GO terms • Machine Learning (GOCat) • k-NN • Similaritybetweeninpurtextand alreadycurated abstracts • KB derivedfrom GOA : ~90’000 instances

6. Best way for extracting GO terms from a set of abstracts ? (2/3) • Twotasks : • Classical categorization(micro reading ~ biocuration) • Redundancy-based QA (macro reading) one abstract/paper GO terms a set of n (=100) abstracts Σ GO terms

7. Best way for extracting GO terms from a set of abstracts ? (3/3) • One benchmark for micro readingevaluation • 1’000 abstracts and GO descriptorsfrom GOA • Two benchmarks for macro readingevaluation • 50 questions derived from a set of biological databases: What molecular functions are affected by [chemical] ? What cellular component is the location of [protein] ?

8. Results + 75/120% for k-NN (sup. learning) • Redundancyhypothesisinsufficient Why/Whereis the power ? Size does or does not matter ?

9. Delugeis self-compensated GOCat EAGL

10. Delugeis self-compensated GOCat EAGL

11. Magic ! The automatic categorization based on a PMID2007 performed in 2011 is of higher quality than a categorization on the same PMID2007 performed in 2007 No concept drift at all and even some improvement!

12. Example in toxicogenomics: CTD vs. GOCat “What molecular functions are affected by Ryanodine ?” GOCat        

13. Example in UniProt “Whatis the subcellular location of protein MEN1 ? ” GOCat        

14. Qualitative evaluation Relevant vs irrelevant : 82% - 18% Guha R., Gobeill J. and Ruch P. Automatic Functional Annotation of PubChem BioAssays

15. Conclusion and future work • Automatic assignment of GO categories ~ 43% • [Camon et al 2003: GO kappa ~ 40%] • Classification model improves faster than drift • [ Consistency of annotation guidelines ] • Next: Effective integration into the EAGLi’ question-answering platform

16. Collaborations • Automatic Functional Annotation of PubChem BioAssays • Generates semantic similarity clusters • Automatically populating large protein datasets Geneswithunvalidatedpredictedfunctions

17. Please visit EAGLi, the Bio-medical question answering engine http://eagl.unige.ch/EAGLi/ !

18. The Gene Ontology Categorizer: http://eagl.unige.ch/GOCat/ Other resources… TWINC (patent retrieval…) http://bitem.hesge.ch

19. Acknowledgments • Swiss-prot group (SIB): Anne-LiseVeuthey, YoannisYenarios • U. Indiana/SCRIPPS: RajarshiGuha / Stephan Schurer • The COMBREX project: Martin Steffen • NextProt: Pascale Gaudet • SNF Grant: EAGL # 120758 • EU FP7: www.KHRESMOI.eu # 257528