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GoPubMed and beyond: Rules and reasoning for ontology-based literature search

Leverage ontology-based rules and reasoning to optimize literature searches, find specific enzyme functions like Levamisole inhibition efficiently in PubMed, and enhance decision-making in bioinformatics. Develop rule-based applications for personalized information systems.

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GoPubMed and beyond: Rules and reasoning for ontology-based literature search

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  1. GoPubMed and beyond: Rules and reasoning for ontology-based literature search

  2. REWERSE:a European Network of Excellence • Reasoning on the Web with Rules and Semantics • Technology • Rule Markup languages • Policy specification, composition and conformance • Composition and typing • Reasoning-aware querying • Evolution and reactivity • Application • Web-based Decision Support for Event, Temporal, and Geographical Data • Towards a Bioinformatics Semantic Web • Personalised Information Systems • Towards a Bioinformatics Semantic Web • Groups: Dresden, Jena, Lisbon, Linkoeping, Edinburgh, Bucarest, Manchester, Paris • Rules and constraints for structure prediction, metabolic pathways, gene expression analysis, ontologies, workflows.

  3. GoPubMed and beyond: Rules and reasoning for ontology-based literature search

  4. Problem PubMed >12M articles

  5. Example Task • Which enzymes does Levamisole inhibit? • It is well known that Levamisole inhibits alkaline phosphatase • It is not well known that Levamisole inhibits phosphofructokinase

  6. PubMed Example • A keyword search for levamisole inhibitor produces well over 100 hits in PubMed. • To find out about specific functions, we have to go through all these papers! • We are interested in the relevant enzymatic functions. • A refined search for Levamisole inhibitor enzymatic activity produces only 5 hits - a lot of relevant papers have been dropped.

  7. GoPubMed Example Query: Levamisole inhibitor Maximum papers: 100 Strict matching 54 papers in biological process 20 in cellular components 72 in molecular function

  8. GoPubMed Example Let’s look for some functions: 70 papers including terms, which are enzyme activities

  9. GoPubMed Example Transferase 8 Kinase 6 Hydrolase 58 Oxidoreductase 2 Lyase 1

  10. GoPubMed Example …alkaline phosphatase inhibitor levamisole… Effects of alkaline phosphatase and its inhibitor Levamisole… Alkaline Phosphatase: 52 papers

  11. GoPubMed Example Phosphofructokinase

  12. GoPubMed Example Levamisole direclty inhibits tumor phosphofructokinase

  13. GoPubMed Example In PubMed the article is listed at position 84! And hence unlikely to be read

  14. GoPubMed andbeyond: Rules and reasoning for ontology-based literature search

  15. Prova

  16. Prova • Rule-based Java scripting for middleware • Combination of object-oriented and declarativeprogramming • Offer a rule-based platform for distributed agent programming. • Expose logic and agent behaviour as rules; • Access data sources via wrappers written in Java • Make all Java API from available packages directly accessible from rules; • Run within the Java runtime; • Enable rapid prototyping of applications;

  17. PubMed XML Output <PubmedArticle> <MedlineCitation Owner="NLM" Status="Publisher"> <PMID>15469972</PMID> <DateCreated> <Year>2004</Year> <Month>10</Month> <Day>7</Day> </DateCreated> <Article> <Journal> <ISSN>0950-1991</ISSN> <JournalIssue PrintYN="N"> <PubDate> <Year>2004</Year> <Month>10</Month> <Day>6</Day> </PubDate> </JournalIssue> <Coden>DEVPED</Coden> <Title>Development (Cambridge, England)</Title> <ISOAbbreviation>Development</ISOAbbreviation> </Journal>…

  18. Output Year: 2004 ArticleTitle: Developmental potential of defined neural progenitors derived from mouse embryonic stem cells. LastName: Plachta FirstName: Nicolas Initials: N ********************** LastName: Bibel FirstName: Miriam Initials: M ********************** LastName: Tucker FirstName: Kerry Lee Initials: KL ********************** LastName: Barde FirstName: Yves-Alain Initials: YA ****************************************** Year: 2004 ArticleTitle: Eye evolution: a question of genetic promiscuity. LastName: Nilsson ForeName: Dan-E Initials: DE ********************** …

  19. %%% XML reading tests :- eval(test_xml1()). test_xml1() :- Document=XML("pubmed.xml"), Root = Document.getDocumentElement(), tagname(Root,Elements), Elements.nodes(Element), subtag(Element,SubElements), SubElements.nodes(SubElement), SubElementNodeName = SubElement.getNodeName(), ChildNodes=SubElement.getChildNodes(), ChildNodes.nodes(ChildNode), evaluate(ChildNode). tagname(Root,Elements):- Elements = Root.getElementsByTagName("MedlineCitation"). subtag(Element,SubElements):- SubElements = Element.getElementsByTagName("PubDate"). subtag(Element,SubElements):- SubElements = Element.getElementsByTagName("Article"). subtag(Element,SubElements):- SubElements = Element.getElementsByTagName("Author"). evaluate(ChildNode):- "Year"=ChildNode.getNodeName(), printout(ChildNode). evaluate(ChildNode):- "Initials"=ChildNode.getNodeName(), printout(ChildNode), println([**********************]). evaluate(ChildNode):- "FirstName"=ChildNode.getNodeName(), printout(ChildNode). evaluate(ChildNode):- "ForeName"=ChildNode.getNodeName(), printout(ChildNode). evaluate(ChildNode):- "LastName"=ChildNode.getNodeName(), printout(ChildNode). evaluate(ChildNode):- "ArticleTitle"=ChildNode.getNodeName(), printout(ChildNode). printout(ChildNode):- ChildNodeName = ChildNode.getNodeName(), DataName = ChildNode.getFirstChild(), StringName = DataName.getNodeValue(), println([ChildNodeName, ": ",StringName]). Code Snippet Prolog rules Java objects Unification

  20. Traverse the GO tree and find all phosphofructokinases Built-in DB access Built-in DB access :-eval(consult("utils.prova")). location(database,“go","jdbc:mysql://comas.soi.city.ac.uk","guest","guest"). location(database,"GO","jdbc:mysql://dbserver","guest","guest"). :-solve(isPhosphofructokinase()). isPhosphofructokinase() :- dbopen("GO",DB), println(["DB open"]), sql_select(DB,term,[id,TermID],[name,Name],[where,"name = 'phosphofructokinase activity'"]), println(["Looking for all children of GO-Term (",TermID,"): ",Name]), findall(TermID,isPhosphofructokinase(DB,TermID),_). isPhosphofructokinase(DB,TermID) :- concat(["term1_id=",TermID],WhereClause), sql_select(DB,term2term,[term2_id,ChildTermID],[where, WhereClause]), concat(["id=",ChildTermID],WhereClause2), sql_select(DB,term,[name,ChildName],[where,WhereClause2]), println([TermID," has child ", ChildName,",",ChildTermID]), isPhosphofructokinase(DB,ChildTermID). isPhosphofructokinase(DB,TermID) :- println([TermID," does not have any children."]). Backtracking Recursion

  21. Output Looking for all children of GO-Term (2153): phosphofructokinase activity 2153 has child 1-phosphofructokinase activity,2154 2154 does not have any children. 2153 has child 6-phosphofructo-2-kinase activity,2155 2155 does not have any children. 2153 has child 6-phosphofructokinase activity,2156 2156 does not have any children. 2153 does not have any children. yes

  22. Messaging and Reaction Rules • Prova is designed to implement agents in a distributed system • Prova is based on theoretical work on multi agent systems • Prova provides predicates to send and receive messages and to realise reaction rules

  23. Code Snippet Built-in agent agent communication: sendMsg and rcvMsg Abstracting from protocols like JMS, Jade or inline :- eval(ex004()). a(1). a(2). b(3). b(4). % Send out messages ex004() :- println(["==========ex004=========="]), iam(Me), sendMsg(XID1,self,Me,queryref,a(I)), rcvMult(XID1,self,Me,reply,a(I)), println(["Inline reaction ",rcvMult(XID1,self,Me,reply,a(I))]), sendMsg(XID2,self,Me,queryref,b(J)), rcvMult(XID2,self,Me,reply,b(J)), println(["Inline reaction ",rcvMult(XID2,self,Me,reply,b(J))]). % Reaction rule to general queryref rcvMsg(XID,Protocol,From,queryref,[X|Xs]|LocalContext) :- println(["Rule reaction 1 ",rcvMsg(XID,Protocol,From,queryref,[X|Xs]|LocalContext)]), derive([X|Xs]), sendMsg(XID,Protocol,From,reply,[X|Xs]|LocalContext). rcvMsg(XID,Protocol,From,queryref,[X|Xs]|LocalContext) :- println(["Rule reaction 2 ",rcvMsg(XID,Protocol,From,queryref,[X|Xs]|LocalContext)]), sendMsg(XID,Protocol,From,end_of_transmission,[X|Xs]|LocalContext). % A testing harness for printing incoming end_of_transmission messages. rcvMsg(XID,Protocol,From,end_of_transmission|Extra) :- println(["end_of_transmission for conversation-id ",XID,": "|Extra]).

  24. Output ==========ex004========== Rule reaction 1 ["rcvMsg","mediator@bioinf-mobile3","self","mediator","queryref",["a",I]] Rule reaction 2 ["rcvMsg","mediator@bioinf-mobile3","self","mediator","queryref",["a",I]] Inline reaction ["rcvMult","mediator@bioinf-mobile3","self","mediator","reply",["a",1]] Inline reaction ["rcvMult","mediator@bioinf-mobile3","self","mediator","reply",["a",2]] end_of_transmission for conversation-id mediator@bioinf-mobile3: ["a",I] Rule reaction 1 ["rcvMsg","mediator@bioinf-mobile6","self","mediator","queryref",["b",N@@16]] Rule reaction 2 ["rcvMsg","mediator@bioinf-mobile6","self","mediator","queryref",["b",N@@16]] Rule reaction 1 ["rcvMsg","mediator@bioinf-mobile8","self","mediator","queryref",["b",N@@16]] Rule reaction 2 ["rcvMsg","mediator@bioinf-mobile8","self","mediator","queryref",["b",N@@16]] Inline reaction ["rcvMult","mediator@bioinf-mobile6","self","mediator","reply",["b",3]] Inline reaction ["rcvMult","mediator@bioinf-mobile6","self","mediator","reply",["b",4]] end_of_transmission for conversation-id mediator@bioinf-mobile6: ["b",N@@16] Inline reaction ["rcvMult","mediator@bioinf-mobile8","self","mediator","reply",["b",3]] Inline reaction ["rcvMult","mediator@bioinf-mobile8","self","mediator","reply",["b",4]] end_of_transmission for conversation-id mediator@bioinf-mobile8: ["b",N@@16] end_of_transmission for conversation-id mediator@bioinf-mobile3: ["a",I] Shutdown complete.

  25. Conclusion • GoPubMed facilitates exploration of literature abstracts with the GeneOntology • Prova implements Prolog-style rules and reasoning with Java • Thanks: • GoPubMed: Andreas Doms, Ralf Delfs, Alex Kozlenkov • Prova: Alex Kozlenkov • Support: EU Projects REWERSE, GeneStream, BioGrid • Contact: • Michael Schroeder: ms@biotec.tu-dresden.de • URLs • www.biotec.tu-dresden.de • www.rewerse.net (REWERSE project including info on bioinfo group) • www.semanticwebrules.org (Prova) • www.gopubmed.org (GoPubMed)

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