1 Semantic Relations for Interpreting DNA Microarray Data and for Novel Hypotheses Generation Dimitar Hristovski, 1 PhD, Andrej Kastrin, 2 Borut Peterlin, 2 MD PhD, Thomas C Rindflesch, 3 PhD 1 Institute of Biomedical Informatics, Medical Faculty, University of Ljubljana, Slovenia 2 Institute of Medical Genetics, University Medical Centre, Ljubljana, Slovenia 3 National Library of Medicine, National Institutes of Health, Bethesda, MD, U.S.A.

2 Introduction Microarray experiments: great potential to support progress in biomedical research, results NOT EASY to interpret, information about functions and relations of relevant genes needs to be extracted from the vast biomedical literature

Related Work Text mining and microarray analysis Literature-based Discovery

4 Proposed Solution Computerized text analysis system Extract semantic relations from literature –SemRep Integrate with microarray experiments Develop tools for: –Interpretation –Novel hypotheses generation

Overall Design Medline GEO SemRep Sem.rels Extraction R Bioconductor scripts Integrated Database= semantic relations + microarrays Interpretation & Discovery Tools semantic relations microarrays

SemRep Extracts semantic relations from biomedical text (implemented in Prolog) Based on UMLS Metathesaurus and Semantic Network – SEMNET RELATION Database of relations extracted from MEDLINE –6. 7 M citations (01/01/1999 through 03/31/2009) –43M sentences –21M relation instances –7M relation types 6

7 Semantic Relations Extracted Wide range of relations in : –Clinical medicine –Molecular genetics –Pharmacogenomics Genetic Etiology: associated_with, predisposes, causes Substance Relations: interacts_with, inhibits, stimulates Pharmacological Effects: affects, disrupts, augments Clinical Actions: administered_to, manifestation_of, treats, Organism Characteristics: location_of, part_of, process_of Co-existence: co-exists_with

8 Examples “… the loss of Mbd1 could lead to autism- like behavioral phenotypes …” Relation: MDB1 causes Autistic Disorder “… Mbd1 can directly regulate the expression of Htr2c, one of the serotonin receptors, …” Relation: MBD1 interacts_with HTR2C

10 Interpretation of Microarrays Find known facts from the literature: Desease related: –Associated genes –Current treatments –… Microarray Genes: –Relations between genes (INHIBITS, STIMULATES, …) –Relations between the genes and anything else

Relations with “Parkinson” as Argument?

What Treats Parkinson?

What (causes, associated_with) Parkinson?

Sentences from which Relations are Extracted

Genes from the Microarray Related to Anything?

16 Novel Hypotheses Generation Based on discovery patterns Discovery patterns: –search templates that have a higher likelihood of returning a new discovery Specific discovery patterns for specific discovery tasks

17 Discovery Patterns Inhibit the upregulated: –Search for substances, genes,... which, according to the literature, inhibit the top N (e.g. 300) genes that are upregulated on a given microarray –Such substances, genes, … might be used to regulate the upregulated genes Stimulate the downregulated: –Search for substances, genes,... which, according to the literature, stimulate the top N (e.g. 300) genes that are downregulated on a given microarray –Such substances, genes, … might be used to regulate the downregulated genes

Discovery Patterns – Graphical View Disease X Maybe_Treats2? Upregulated Downregulated Genes Y1 Genes Y2 Drug Z1 (or substance) Drug Z2 (or substance) Inhibits Stimulates Maybe_Treats1? Microarray Literature

19 Results – Inhibit the Upregulated Parkinson microarray GSE8397 HSP27 (HSPB1) gene is upregulated on the microarray We identified paclitaxel and quercetin as substances that inhibit the expression of this gene

Inhibit the Upregulated

21 Results – Stimulate the Downregulated NR4A2 downregulated on the microarray We found out that: – Pramipexol stimulates expression of NR4A2 – NR4A2 is associated with Parkinson disease

Explaining a Relation - Closed Discovery

Closed Discovery – Aligned Relations

Evaluation Estimate – based on [Masseroli, BMC Bioinformatics 2006]: Extract known facts – baseline precision on 2,042 extracted relations: –Gene – Disease (causes, assoc_with, …) P=74.2% –Gene – Gene (inhibits, stimulates, …) P=41.95% Propose Argument-Predicate distance for filtering (Gene-Gene): –At distance no more than 1: P=70.75%; R=43.6% –At distance no more than 2: P=55.88%; R=66.28% We use Argument-Predicate distance for ranking of semantic relations and we show relations more likely to be correct first.

25 Conclusion A new bioinformatics tool for interpretation and novel hypotheses generation Based on integration of semantic relations extracted from literature with microarrays Available at:

Syntactic Processing Mbd1 can directly regulate the expression of Htr2c MedPost tagger and shallow parser [ NP [head([… inputmatch(mdb1),tag(noun)])],... [verb([inputmatch(regulate),lexmatch(regulate),tag(verb)])],... NP [… head([… inputmatch(htr2c),tag(noun)])] ] 26

Semantic Processing Identify concepts: MetaMap and ABGene [ NP [head([… semtype(gngm),entrez(MBD1,4152)])],... [verb([inputmatch(regulate),lexmatch(regulate),tag(verb)])],... NP [… head([… semtype(gngm),entrez(HTR2C,3358)])] ] 27

Semantic Processing Identify concepts: MetaMap and ABGene [ NP [head([… semtype(gngm),entrez(MBD1,4152)],... [verb([inputmatch(regulate),lexmatch(regulate),tag(verb)])],... NP [… head([… semtype(gngm),entrez(HTR2C,3358])] ] Match semantic type patterns to ontology: INTERACTS_WITH 28

Semantic Processing Identify concepts: MetaMap and ABGene [ NP [head([… semtype(gngm),entrez(MBD1,4152)],... [verb([inputmatch(regulate),lexmatch(regulate),tag(verb)])],... NP [… head([… semtype(gngm),entrez(HTR2C,3358])] ] Match semantic type patterns to ontology: INTERACTS_WITH 29

Semantic Processing Identify concepts: MetaMap and ABGene [ NP [head([… semtype(gngm),entrez(MBD1,4152)],... [verb([inputmatch(regulate),lexmatch(regulate),tag(verb)])],... NP [… head([… semtype(gngm),entrez(HTR2C,3358])] ] Match semantic type patterns to ontology: INTERACTS_WITH Apply indicator rule: Verb(regulate)  INTERACTS_WITH 30

Semantic Processing Identify concepts: MetaMap and ABGene [ NP [head([… semtype(gngm),entrez(MBD1,4152)],... [verb([inputmatch(regulate),lexmatch(regulate),tag(verb)])],... NP [… head([… semtype(gngm),entrez(HTR2C,3358])] ] Match semantic type patterns to ontology: INTERACTS_WITH Apply indicator rule: Verb(regulate)  INTERACTS_WITH 31

Semantic Processing Identify concepts: MetaMap and ABGene [ NP [head([… semtype(gngm),entrez(MBD1,4152)],... [verb([inputmatch(regulate),lexmatch(regulate),tag(verb)])],... NP [… head([… semtype(gngm),entrez(HTR2C,3358])] ] Match semantic type patterns to ontology: INTERACTS_WITH Apply indicator rule: Verb(regulate)  INTERACTS_WITH Substitute concepts for semantic types: 32

Semantic Processing Identify concepts: MetaMap and ABGene [ NP [head([… semtype(gngm),entrez(MBD1,4152)],... [verb([inputmatch(regulate),lexmatch(regulate),tag(verb)])],... NP [… head([… semtype(gngm),entrez(HTR2C,3358])] ] Match semantic type patterns to ontology: INTERACTS_WITH Apply indicator rule: Verb(regulate)  INTERACTS_WITH Substitute concepts for semantic types: 33

Semantic Processing Identify concepts: MetaMap and ABGene [ NP [head([… semtype(gngm),entrez(MBD1,4152)],... [verb([inputmatch(regulate),lexmatch(regulate),tag(verb)])],... NP [… head([… semtype(gngm),entrez(HTR2C,3358])] ] Match semantic type patterns to ontology: INTERACTS_WITH Apply indicator rule: Verb(regulate)  INTERACTS_WITH Substitute concepts for semantic types: MBD1 INTERACTS_WITH HTR2C 34