1. Application of the NLP techniques to IE and IR CREST

2. Outline Background Building NLP resources GENIA Extracting Disease-Gene Associations from MEDLINE H-invitational Extracting DGAs by machine learning An IR system for predicate-argument relations MEDUSA

3. Application to the Biomedical domain Plenty of text MEDLINE database: 12 million abstracts Needs of effective IE and IR Domain knowledge Gene ontology, KEGG, UMLS, ICD, … Other Information sources A variety of molecular databases DNA sequences, motifs, diseases, molecular interactions, etc…

4. Developing NLP resources Resources for NLP research Domain knowledge Training data for ML-based techniques Test data for evaluating the transferability of a system We are now developing… GENIA Ontology Corpus

5. GENIA corpus 4,000 MEDLINE abstracts Selected by MeSH Terms (Human, Blood cells, Transcription factors) XML format Contents Named-entity (Kim et al 2003) Part-of-speech (Tateisi et al 2004) Parse tree Co-reference (Institute of Infocomm Research, Singapore)

6. The peri-kappa B site mediates human immunodeficiency virus type 2 enhancer activation in monocytes … GENIA named-entity corpus Terms are annotated based on the semantic classes in the GENIA ontology Size 2,000 abstracts Number of the terms: 92,723 Vocabulary size: 36,568 DNA virus cell_type

7. GENIA part-of-speech corpus Each token is annotated with its part-of-speech tag. Size 2,000 abstracts 20,544 sentences 50,1054 words (about half the size of Penn Treebank) The peri-kappa B site mediates human immunodeficiency virus type 2 enhancer activation in monocytes … DT NN NN NN VBZ JJ NN NN NN CD NN NN IN NNS

8. GENIA treebank Based on the standard of the Penn TreeBank Size 200 abstracts (1500 abstracts at the end of this fiscal year) CD3-episilon expression is controlled by a downstream T lymphocyte-specific enhancer element NP ADJP NP PP VP S

9. GENIA corpus Used in more than 240 institutions Japan (28), Asia (54), North America (63), Europe (62), etc… De facto standard for evaluating biomedical named-entity recognition systems BioNLP workshop at Coling 2004 Named-entity recognition shared task Institute for Infocomm Research (Singapore), Stanford University (USA), University of Edinburgh (UK), University of Wisconsin-Madison (USA), Pohang University of Science and Technology (Korea), University of Alberta (Canada), University Duisburg-Essen (Germany), Korea University (Korea), National Taiwan University (Taiwan),

10. Outline Background Building NLP resources GENIA Extracting Disease-Gene Associations from MEDLINE H-invitational Extracting DGAs by machine learning An IR system for predicate-argument relations MEDUSA

11. H-Invitational Disease Edition Text-mining Scoring system (PANDA) Known disease gene Genomic region of interest (GROI) List of genes Genes with high score SNPs 1)Public 2)Private Gene expression 1)Public 2)Private AND/OR Final Result H-InvDB Other DB Literature (PubMed) Dictionary Specific disease Select specific disease June 25, 2004 Disease group, JBIRC Synthetic analysis

12. Disease-Gene Associations extracted from MEDLINE DGA explorer (demo)

13. Text 1.5 million MEDLINE abstracts Selected by MeSH Terms Disease Category AND (Amino Acids, Peptides, and Proteins OR Genetic Structures) Parsing All the sentences were parsed by the HPSG parser Using a PC cluster (100 processors with GXP) Time: 10 days

14. Disease-Gene Associations in texts These results suggested that targeted disruption of Cyp19 caused anovulation and precocious depletion of ovarian follicles Furthermore, AML cells with methylated p15(INAK4B) tended to express higher levels of DNMT1 and 3B.

15. Training data All foals with OLWS were homozygous for the Ile118Lys EDNRB mutation, and adults that were homozygous were not found. Dominant radial drusen and Arg345Trp EFEMP1 mutation. The 5 year overall survival (OS) and event-free survival (EFS) were 94 and 90 +/- 8%, respectively, with a median follow-up of 48 months. These data may indicate that formation of parathyroid adenoma in young patients is related to a mechanism involving EGFR. All co-occurrences are classified into relevant or irrelevant by a domain expert.

16. Maximum entropy learning Log-linear model Feature function Weight Features Bag-of-words Local context Gene/disease name Predicate-argument structures :

17. Features of predicate- argument structures (1) Dedifferentiation of adenoid cystic carcinoma: report of a case implicating p53 gene mutation. X gene/disease ARG2

18. Features of predicate- argument structures (2) These results suggested that targeted disruption of Cyp19 caused anovulation and precocious depletion of ovarian follicles. Furthermore, AML cells with methylated p15(INAK4B) tended to express higher levels of DNMT1 and 3B. X disease/gene ARG2ARG1 gene/disease

19. Extraction accuracy Training/test data: 2,253 sentences 10-fold cross validation featuresrecallprecisionf-score N/A1.00.3510.520 + bag of words0.7330.6820.706 + local context0.7330.6950.714 + predicate- argument structures 0.7590.7100.733

20. Outline Background Building NLP resources GENIA Extracting Disease-Gene Associations from MEDLINE H-invitational Extracting DGAs by machine learning An IR system for predicate-argument relations MEDUSA

21. MEDUSA: An IR system for predicate-argument structures Ex. Search a sentence in which the subject of the verb activate is protein. Simple: Since the PHO2 Asp-230 mutant mimics Ser-230-phosphorylated PHO2, we postulate that only phosphorylated PHO2 protein could activate the transcription of PHO5 gene. With a relative pronoun: Transcription initiation by the sigma(54)-RNA polymerase holoenzyme requires an enhancer-binding protein that is thought to contact sigma(54) to activate transcription. Coordination: Full-strength Straufen protein lacking this insertion is able to assocaite with osker mRNA and activate its translation, but fais to localize the RNA to the posterior.

22. MEDUSA demonstration 100,000 MEDLINE abstracts Parsed by Enju Genes and diseases are annotated by using the UMLS dictionary

23. Summary GENIA corpus Parts of speech, Named-entities, Parse trees Extracting gene-disease associations from MEDLINE Machine learning with HPSG parse results An IR system for predicate-argument structures MEDUSA

24. Software and resource GENIA Named entity corpus Part-of-speech corpus Parse tree corpus Co-reference (Singapore) Part-of-speech tagger Named entity tagger (soon) HPSG parse results (100,00 MEDLINE abstracts) Enju (HPSG parser) MEDUSA LiLFeS Amis