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A Preliminary Report on Answering Complex Queries related to Drug Discovery using Answer Set Programming 1 National Library of Medicine, National Institutes of Health, USA 2 Department of Biostatistics, Harvard School of Public Health, USA 3 Dept. of Mathematics and Computing Science, University of Groningen, The Netherlands 4 Faculty of Engineering and Natural Sciences, Sabancı University, Turkey 5 Department of Computer Science, University of Toronto, Canada Abstract. We introduce a new method for integrating relevant parts of knowl-edge extracted from biomedical ontologies and answering complex queries re-lated to drug safety and discovery, using Semantic Web technologies and answerset programming. The applicability of this method is illustrated in detail on someparts of existing biomedical ontologies. Its effectiveness is demonstrated by com-puting an answer to a real-world biomedical query that requires the integration ofNCBI Entrez Gene and the Gene Ontology.
Improvements in Web technologies have brought about various forms of data, and thusWWW has been a huge and easy-to-reach source of knowledge. Particularly recentadvances in health and life sciences (e.g., human genome project) have led to generationof a large amount of data. In order to facilitate access to its desired parts, such a big massof data has been stored in structured forms (like databases or ontologies). For instance,some data/information about drugs is being stored in ontologies, like DRUGBANK andPHARMGKB, available on WWW; and the genes targeted by the drug Epinephrine canbe found by searching such a drug ontology using the keyword “Epinephrine.” On the other hand, storing heterogeneous data independent from each other and at different locations has made it difficult to automate high-level reasoning about thestored data. For instance, it is possible to find an answer to the query “What are thegenes targeted both by Epinephrine and by Isoproterenol?” only after several steps:considering that a drug (and also a gene) might have been stored in different ontologiesunder different names, first for each drug a list of genes targeted by that drug could befound, and next these two lists of genes are compared to identify the common ones, bycomparing these two lists of genes. Such complex queries, which require appropriate in-tegration of knowledge stored in different places and in various forms, can be answeredby current Web technologies most of the time only by some direction/reasoning of hu-mans. This slows down vital research, like drug discovery, that requires comparativedata analysis and high-level reasoning and decision making.
Motivated by these challenges, this paper studies the problem of integrating vari- ous data sources to be able to perform high-level reasoning tasks, including answering complex queries using both Semantic Web technologies and Answer Set Programming(ASP) [1–4]. The idea is to build a rule layer using ASP over ontologies described withsome Semantic Web technologies. The rule layer not only provides rules to link partsof the ontologies but also provides some background knowledge to be able to performvarious reasoning tasks, such as query answering.
That most of the information about biomedical ontologies are actually defaults and that most biomedical ontologies contain incomplete knowledge motivated us to use anonmonotonic formalism to build a rule layer over ontologies. That experts might wantto express preferences as well as constraints while querying the knowledge stored inontologies to be able to discover new knowledge, and that ASP provides an expressivelanguage to express them and efficient solvers, like DLVHEX6 [5] built over DLV,7 toreason about them motivated us to use ASP as such a nonmonotonic formalism.
To experiment with our ASP approach to integrating biomedical ontologies and reason-ing about them, and to illustrate its applicability, we have developed three ontologies,namely a gene ontology, a disease ontology, and a drug ontology. We have built theseontologies from existing knowledge from various data sources available on the Web.
These ontologies are written in RDF(S). To develop our disease ontology, first we se-lected a set of diseases. The names (and their synonyms) of each disease are takenfrom PHARMGKB database.8 Information about the symptoms of these diseases isobtained from the Medical Symptoms and Signs of Disease web page.9 Informationabout the genes related to each disease are also extracted from PHARMGKB. Each dis-ease is classified in some category relative to the information available at the Genesand Diseases web page.10 Some components of the disease ontology is shown in Ta-ble 1. We have prepared the other two ontologies in a similar way, using PHARMGKB,UNIPROT,11 GENE ONTOLOGY (GO),12 GENENETWORK database,13 DRUGBANK,14and the Medical Symptoms and Signs of Disease web page.
Integrating Knowledge Extracted from Different Ontologies DLVHEX provides constructs to import external theories that may be in different for-mats. For instance, consider as an external theory our drug ontology described in RDF.
All triples from this theory can be exported using the external predicate &rdf: 6 http://con.fusion.at/dlvhex/7 http://www.dbai.tuwien.ac.at/proj/dlv/8 http://www.pharmgkb.org/ .
9 http://www.medicinenet.com/symptoms_and_signs/article.htm .
10 http://www.ncbi.nlm.nih.gov/disease/ .
11 http://www.ebi.uniprot.org/index.shtml .
12 http://www.geneontology.org .
13 http://humgen.med.uu.nl/˜lude/genenetwork/ .
14 http://redpoll.pharmacy.ualberta.ca/drugbank/ .
Table 1. The disease “Asthma” described in our disease ontology treatedBy drugs Isoproterenol, Flunisolide, Salbutamol triple_drug(X,Y,Z) :- &rdf["URI for Drug Ontology"](X,Y,Z).
Not all triples may be relevant to the query asked by the user. For instance, if one asks for the names of drugs listed in the ontology, then only the triples that describethe names of drugs are sufficient to answer this query. The names of drugs, out of allproperties about drugs described in drug.rdf, can be extracted by the following rule: drug_name(A) :- triple_drug(_,"drugproperties:name",A).
If the query were about gene-gene interactions, then we could extract the relevant gene_gene(G1,G2) :- triple_gene(X,"geneproperties:name",G1), triple_gene(X,"geneproperties:related_genes",B), triple_gene(B,Z,Y), Z!="rdf:type", triple_gene(Y,"geneproperties:name",G2).
Once necessary parts of ontologies are extracted from ontologies, one can define further concepts to integrate these knowledge. For instance, once we extract the gene-gene interactions, we can obtain all chains of gene-gene interactions for a gene targetedby a drug, by defining the transitive closure of gene gene: tc_gene_gene(X,Y) :- gene_gene(X,Y).
tc_gene_gene(X,Y) :- gene_gene(X,Z), tc_gene_gene(Z,Y).
Now let us relate this information to a gene G targeted by a drug D by finding every gene G1 that is related to G by means of a chain of interactions: drugTargetedGene_interacts_gene(D,G,G1) :- drug_targets(D,G), tc_gene_gene(G,G1).
With the help of Devrim G¨oz¨uac¸ık (a medical doctor and a molecular biologist), wehave identified a set of meaningful queries about drugs, genes, diseases, towards drugsafety and discovery. We present here only three of them: Q6 What are the sideeffects that are shared by all the drugs that treat a disease D? Q12 Is there a drug that has no toxicity information?Q14 Does a drug R alleviate at least 1 symptom of a disease D and have at most 2 We integrate relevant parts of ontologies, and formulate these queries as follows.
Q6 What are the sideeffects that are shared by all the drugs that treat a disease D? For the disease Asthma, this query can be formulated as follows: answer :- sideeffect(S), common_sideeffect("Asthma",S).
Here common sideeffect is defined as follows: -common_sideeffect(D,S) :- not drug_sideeffect(R,S), common_sideeffect(D,S) :- not -common_sideeffect(D,S), Here is a part of the answer DLVHEX finds to the query above: Q12 Is there a drug that has no toxicity information? To answer this query, we define a new concept of “unknown” toxicity: unknown_toxicity_drug(X) :- drug_synonym(R,X), not drug_istoxic(R), not -drug_istoxic(R).
where drug istoxic(R) describes that the drug R is toxic, and -drug istoxic(R)describes that the drug R is not toxic: drug_istoxic(R) :- triple_drug(X,"drugproperties:name",R), triple_drug(X,"drugproperties:is_toxic","yes").
drug_istoxic(R) :- drug_synonym(R,R1), drug_istoxic(R1).
-drug_istoxic(R) :- triple_drug(X,"drugproperties:name",R), triple_drug(X,"drugproperties:is_toxic","no").
-drug_istoxic(R) :- drug_synonym(R,R1), -drug_istoxic(R1).
:- not unknown_toxicity_drug("Isoproterenol").
DLVHEX returns an answer set; therefore the answer to the query above is positive.
Q14 Does a drug R alleviate at least 1 symptom of a disease D and have at most 2 To answer this query we define a new concept: 1 <= #count{S:drug_symptom(R,S),disease_symptom(D,S)}, #count{S:drug_sideeffect(R,S),disease_symptom(D,S)}<=2.
:- not a_drug_disease_relation("Isoproterenol", "Substance Related Disorders").
DLVHEX returns no answer set; therefore the answer to the query above is negative.
From Glycosyltransferase to Congenital Muscular Dystrophy To investigate the effectiveness of our approach to answering real-world queries, wehave considered a slight modification of the complex query studied in [6]: Find all the genes annotated with the molecular function glycosyltransferaseor any of its descendants and associated with any form of congenital musculardystrophy.
and tried to reproduce the same results. In the query of [6] the GO ID for glycosyltrans-ferase is given. The query above requires integration of NCBI Entrez Gene (EG) andthe Gene Ontology (GO).
To find an answer to this query, we have used the RDF version of GO that is released on February 6, 2008; it contains 416700 RDF triples. We have used an RDF version ofEG that contains 673180 RDF triples.
The computation of an answer consists of two parts: extracting relevant knowledge from each ontology and integrating them. We have extracted from GO the molecularfunction glycosyltransferase and its descendants by the rules mf_isa(Y) :- triple_go(Y,"go:name",YN), &strstr[YN,"glycosyltransferase"].
mf_isa(Y) :- triple_go(Y,"go:synonym",YN), &strstr[YN,"glycosyltransferase"].
mf_isa(X) :- triple_go(X,"go:is_a",Y), mf_isa(Y).
mf_isa(X) :- triple_go(X,"go:synonym",XN), triple_go(Z,"go:name",XN), triple_go(Z,"go:is_a",Y), mf_isa(Y).
The first two rules extract the molecular functions whose names or synonyms containthe string “glycosyltransferase”. The last two rules extract the descendants of thesemolecular functions, considering their synonyms.
Similarly, we have extracted from EG the diseases with any form of congenital gene_disease(Y,D) :- triple_eg(Y,"eg:has_OMIM_record",Z), triple_eg(Z,"eg:has_textual_description",D), &strstr[D,"congenital"], &strstr[D,"muscular"], After that we have integrated the extracted knowledge by the rules gene_mf_disease(Y,XI,D) :- gene_disease(Y,D), triple_eg(Y,"eg:has_GeneOntology_annotation",X), mf_isa(XI), triple_eg(X,"eg:has_GO_ID",XI).
and computed the following answer (the same as in [6]) to the query: gene_mf_disease("http://www.ncbi.nlm.nih.gov/dtd/NCBI_Entrezgene.
dtd/9215", "http://www.geneontology.org/go#GO:0008375", "Muscular dystrophy, congenital, type 1D") DLVHEX extracts relevant knowledge from the ontologies, integrates them, and computes the answer above in 9 minutes, on a machine with Intel Centrino 1.8GHzCPU and 1 GB of RAM running on Windows XP.
We have studied integrating relevant parts of knowledge extracted from biomedical on-tologies, and answering complex queries related to drug safety and discovery, using Se-mantic Web technologies and Answer Set Programming (ASP). We have illustrated theapplicability of this method on some ontologies extracted from existing biomedical on-tologies, and its effectiveness by computing an answer to a real-world biomedical querythat requires the integration of NCBI Entrez Gene and the Gene Ontology. We have alsocompared our approach with the existing Semantic Web technologies that support rep-resenting and answering queries. We have observed about these technologies that, dueto lack of support for rules or for some concepts (e.g., transitive closure, negation as fail-ure, cardinality constraints), some queries can not be represented concisely and somequeries can not be represented at all. In this sense, the ASP-approach provides a moreexpressive formalism to represent rules, concepts, constraints, and queries.
Devrim G¨oz¨uac¸ık helped us identify some of the complex queries. Thomas Krennwall-ner and Roman Schindlauer helped us with installing/using DLVHEX. RACER Systemsprovided us a free, educational version of RACERPRO,15 to be used in connection withDLVHEX. Anonymous reviewers provided useful comments on an earlier draft. Thisresearch was supported in part by the Intramural Research Program of the NationalInstitutes of Health (NIH), National Library of Medicine (NLM).
1. Lifschitz, V.: Action languages, answer sets and planning. In: The Logic Programming Paradigm: a 25-Year Perspective. Springer (1999) 2. Marek, V., Truszczy´nski, M.: Stable models and an alternative logic programming paradigm.
In: The Logic Programming Paradigm: a 25-Year Perspective. Springer (1999) Logic programs with stable model semantics as a constraint programming paradigm. Annals of Mathematics and Artificial Intelligence 25 (1999) 4. Baral, C.: Knowledge Representation, Reasoning and Declarative Problem Solving. Cam- 5. Eiter, T., G.Ianni, R.Schindlauer, H.Tompits: Effective integration of declarative rules with external evaluations for Semantic-Web reasoning. In: Proc. of ESWC. (2006) 6. Sahoo, S.S., Zeng, K., Bodenreider, O., Sheth, A.: From “glycosyltransferase” to “congenital muscular dystrophy”: Integrating knowledge from NCBI Entrez Gene and the Gene Ontology.
In: Proc. of Medinfo. (2007)

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