Formalising	  Uncertainty:	  	     An	  Ontology	  of	  Reasoning,	  Certainty	  and	  A9ribu<on	  (ORCA)	  Anita	  de	  W...
Outline	  •  Background:	  	      –  Metadiscourse,	  epistemic	  modality,	  and	  knowledge	         a9ribu<on,	  oh	  m...
Background	  
Scien<sts	  make	  uncertain	  claims	  Uncertainty	  These	  miRNAs	  neutralize	  p53-­‐mediated	  CDK	  inhibi;on,	  po...
But	  uncertainty	  gets	  lost	  while	  ci<ng	  Uncertainty	  These	  miRNAs	  neutralize	  p53-­‐mediated	  CDK	  inhib...
Uncertainty	  in	  ac<on:	         “[Y]ou	  can	  transform	  ..	  fic<on	  into	  fact	  just	  by	  adding	  or	         ...
Uncertainty	  =	  Hedging:	  •  Why	  do	  authors	  hedge?	      –  Make	  a	  claim	  ‘pending	  […]	  acceptance	  in	 ...
Our	  Model	  
Our	  model	  for	  epistemic	  evalua<ons:	  For	  a	  Proposi<on	  P,	  an	  epistemically	  marked	  clause	  E	  is	  ...
Adding	  Epistemic	  Evalua<on	  Together,	  Lats2	  and	  ASPP1	  shunt	  p53	  to	  proapopto<c	                        ...
Finding	  hedges	  in	  text	  [9]:	  •  Modal	  auxiliary	  verbs	  (e.g.	  can,	  could,	  might)	  	  •  Qualifying	  a...
Manual	  iden<fica<on:	  Value	                        Modal	          Repor6ng	         Ruled	  by	   Adverbs/ Referenc No...
Most	  prevalent	  clause	  type:	  	                  “These	  results	  suggest	  that...”	  Adverb/Connec<ve	          ...
Repor<ng	  verbs	  vs.	  epistemic	  value:	  Value	  =	  0	        establish,	  (remain	  to	  be)	  elucidated,	  	  (un...
Finding	  Claimed	  Knowledge	  Updates	  [10]:	  Defini<on:	  	  1)	  A	  CKU	  expresses	  a	  proposi<on	  about	  biolo...
Automa<c	  hedge	  detec<on	  with	   The	  Xerox	  Incremental	  Parser:	                               Concept-­‐matchin...
Result:	  CKUs	  appear	  throughout	  the	  paper                                                         	              ...
The	  Xerox	  Incremental	  Parser:	                               Concept-­‐matching:                                    ...
The	  formal	  model	            ©	  Jodi	  Schneider,	  	  with	  thanks	  to	  Siggi	  Handschuh	  
orca	  [11]	  	  vocab.deri.ie/orca	  	  
Example	  Usage	  	  	  	  <claim>	  orca:hasBasis	  orca:Data	  .	  
Basis	  
Source	  
ConfidenceLevel	  
How	  to	  represent	  the	  hierarchy?	      lack	  of	  knowledge	  <	  hypothe;cal	  knowledge	  	      <	  dubita;ve	 ...
Transi<ve	  proper<es	  used	  for	        ConfidenceLevel	  
ConfidenceLevel	  &	  its	  Rela<onships	  
Possible	  Applica<ons	  
Add	  knowledge	  value/basis/source	  	                                    to	  a	  bio-­‐event	                         ...
E.g.	  to	  augment	  Medscan	  [13]	  Biological	  statement	  with	  Medscan/                    MedScan	  Analysis:	   ...
Or	  Biological	  Exchange	  Language	  [14]:	  	  Biological	  statement	  with	                BEL	  representa6on:	    ...
Using	  ORCA	  for	  Nanopublica<ons	  [15]:	  •  Use	  to	  indicate	  Strength,	  Basis,	  Source	  of	     Asser<ons:	 ...
Next	  steps:	  	  •  Con<nuing	  experiments	  with	  automated	     detec<on	  •  Can	  be	  used	  in	  Claim-­‐Evidenc...
Thank	  you!	  •  Funding:	  	                                 •  Discussion	  partners:	  	      –  Elsevier	  Labs	     ...
Ques<ons?	  	                       	            Anita	  de	  Waard	      a.dewaard@elsevier.com	   h9p://elsatglabs.com/l...
References	  [1]	  Latour,	  B.	  and	  Woolgar,	  S.,	  Laboratory	  Life:	  the	  Social	  Construc<on	  of	  Scien<fic	 ...
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Talk at ISWC 2012 Workshop on Semantic Technologies Applied to Biomedical Informatics and Individualized Medicine (SATBI+SWIM 2012)

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Formalising Uncertainty: An Ontology of Reasoning, Certainty and Attribution (ORCA), November 12, 2012, Boston, MA

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Talk at ISWC 2012 Workshop on Semantic Technologies Applied to Biomedical Informatics and Individualized Medicine (SATBI+SWIM 2012)

  1. 1. Formalising  Uncertainty:     An  Ontology  of  Reasoning,  Certainty  and  A9ribu<on  (ORCA)  Anita  de  Waard   Jodi  Schneider  Disrup<ve  Technologies  Director   PhD  Researcher  Elsevier  Labs,  Jericho,  VT,  USA   DERI,  Galway,  Ireland      
  2. 2. Outline  •  Background:     –  Metadiscourse,  epistemic  modality,  and  knowledge   a9ribu<on,  oh  my!   –  Some  related  work:  genre  studies,  linguis<cs,  NLP  •  Our  model:   –  What  it  models   –  The  ontology   –  How  can  we  find  this  in  text?  •  Possible  applica<ons:     –  Possible  uses   –  Next  steps  
  3. 3. Background  
  4. 4. Scien<sts  make  uncertain  claims  Uncertainty  These  miRNAs  neutralize  p53-­‐mediated  CDK  inhibi;on,  possibly  through  direct  inhibi;on  of  the  expression  of  the  tumor-­‐suppressor  LATS2.    
  5. 5. But  uncertainty  gets  lost  while  ci<ng  Uncertainty  These  miRNAs  neutralize  p53-­‐mediated  CDK  inhibi;on,  possibly  through  direct  inhibi;on  of  the  expression  of  the  tumor-­‐suppressor  LATS2.     Certainty   Two  oncogenic  miRNAs,  miR-­‐372  and   miR-­‐373,  directly  inhibit  the  expression  of   Lats2,  thereby  allowing  tumorigenic  growth   in  the  presence  of  p53  (Voorhoeve  et  al.,   2006)  
  6. 6. Uncertainty  in  ac<on:   “[Y]ou  can  transform  ..  fic<on  into  fact  just  by  adding  or   subtrac<ng  references”,  Bruno  Latour  [1]•  Voorhoeve  et  al.,  2006:   These  miRNAs  neutralize  p53-­‐  mediated  CDK   inhibi<on,  possibly  through  direct  inhibi<on  of  the  expression  of  the  tumor   suppressor  LATS2.  •  Kloosterman  and  Plasterk,  2006:   In  a  gene<c  screen,  miR-­‐372  and  miR-­‐373   were  found  to  allow  prolifera<on  of  primary  human  cells  that  express   oncogenic  RAS  and  ac<ve  p53,  possibly  by  inhibi<ng  the  tumor  suppressor   LATS2  (Voorhoeve  et  al.,  2006).  •  Yabuta  et  al.,  2007:     [On  the  other  hand,]  two  miRNAs,  miRNA-­‐372  and-­‐373,   func<on  as  poten6al  novel  oncogenes  in  tes<cular  germ  cell  tumors  by   inhibi<on  of  LATS2  expression,  which  suggests  that  Lats2  is  an  important   tumor  suppressor  (Voorhoeve  et  al.,  2006).    •  Okada  et  al.,  2011:   Two  oncogenic  miRNAs,  miR-­‐372  and  miR-­‐373,  directly   inhibit  the  expression  of  Lats2,  thereby  allowing  tumorigenic  growth  in  the   presence  of  p53  (Voorhoeve  et  al.,  2006).  
  7. 7. Uncertainty  =  Hedging:  •  Why  do  authors  hedge?   –  Make  a  claim  ‘pending  […]  acceptance  in  the  community’  [2]   –  ‘Create  A  Research  Space’  –  hedging  allows  authors  to  insert  themselves  into   the  discourse  in  a  community  [3]   –  ‘the  strongest  claim  a  careful  researcher  can  make’  [4]  •  Hedging  cues,  specula<ve  language,  modality/nega<on:   –  Light  et  al  [5]:  finding  specula<ve  language   –  Wilbur  et  al  [6]:  focus,  polarity,  certainty,  evidence,  and  direc<onality   –  Thompson  et  al  [7]:  level  of  specula<on,  type/source  of  the  evidence  and   level  of  certainty      •  Sen<ment  detec<on  (e.g.  Kim  and  Hovy  [8]  a.m.o.):     –  Holder  of  the  opinion,  strength,  polarity  as  ‘mathema<cal  func<on’  ac<ng  on   main  proposi<onal  content     –  Wide  applica<ons  in  product  reviews;  but  not  (yet)  in  science!  
  8. 8. Our  Model  
  9. 9. Our  model  for  epistemic  evalua<ons:  For  a  Proposi<on  P,  an  epistemically  marked  clause  E  is  an  evalua<on  of  P,    where    EV,  B,  S(P),  with:   –  V  =  Value:   3  =  Assumed  true,  2  =  Probable,  1  =  Possible,  0  =  Unknown,     (-­‐  1=  possibly  untrue,  -­‐  2  =  probably  untrue,  -­‐3  =  assumed  untrue)   –  B  =  Basis:   Reasoning   Data     –  S  =  Source:   A  =  speaker  is  author  A,  explicit   IA  =  speaker  author,  A,  implicit   N  =  other  author  N,  explicit   NN  =  other  author  NN,  implicit     Model  suggested  by  Eduard  Hovy,     Informa;on  Sciences  Ins;tute  University  South  Califormia  
  10. 10. Adding  Epistemic  Evalua<on  Together,  Lats2  and  ASPP1  shunt  p53  to  proapopto<c   Value  =  3  promoters  and  promote  the  death  of  polyploid  cells  [1].  (…)   Source  =  N     Basis  =  0    Further  biochemical  characteriza<on  of  hMOBs  showed  that     Value  =  3  only  hMOB1A  and  hMOB1B  interact  with  both  LATS1  and   Source  =  N  LATS2  in  vitro  and  in  vivo  [39].  (…)   Basis  =  Data        Our  findings  reveal  that  miR-­‐373  would  be  a  poten<al   Value  =  1  oncogene  and  it  par<cipates  in  the  carcinogenesis  of  human   Source  =  Author  esophageal  cancer  by  suppressing  LATS2  expression.       Basis  =  Data        Furthermore,  we  demonstrated  that  the  direct  inhibi<on  of   Value  =  2  (3?)  LATS2  protein  was  mediated  by  miR-­‐373  and  manipulated  the   Source  =  Author  expression  of  miR-­‐373  to  affect  esophageal  cancer  cells  growth.     Basis  =  Data        
  11. 11. Finding  hedges  in  text  [9]:  •  Modal  auxiliary  verbs  (e.g.  can,  could,  might)    •  Qualifying  adverbs  and  adjec<ves  (e.g.  interes;ngly,   possibly,  likely,  poten;al,  somewhat,  slightly,   powerful,  unknown,  undefined)  •  References,  either  external  (e.g.  ‘[Voorhoeve  et  al.,   2006]’)  or  internal  (e.g.  ‘See  fig.  2a’).    •  Repor<ng/epistemic  verbs  (e.g.  suggest,  imply,   indicate,  show)     –  either  within  the  clause:  ‘These  results  suggest  that...’     –  or  in  a  subordinate  clause  governed  by  repor<ng-­‐verb   matrix  clause  ‘{These  results  suggest  that}  indeed,  this   represents  the  true  endogenous  ac;vity.’  
  12. 12. Manual  iden<fica<on:  Value   Modal   Repor6ng   Ruled  by   Adverbs/ Referenc None   Total     Aux     Verb   RV   Adjec6ves   es  Total  value  =  3   1  (0.5%)   81  (40%)   24  (12%)   7  (4%)   41  (20%)   47  (24%)  201(100%)  Total  Value  =  2   29  (51%)   23  (40%)   1  (2%)   4(7%)   57(100%)  Total  Value  =  1   9(27%)   11(33%)   11(33%)   1(3%)   1(3%)   33(100%)  Total  Value  =  0   9  (64%)   3  (21%)   1(7%)   1(7%)   14(100%)  Total  No  Modality   16(37%)   3(7%)   0   3(7%)   22(50%)   44(100%)  Overall  Total   10  (2%)   146(23%)   64(10%)   10(2%)   50(8%)   69(11%)  640(100%)  
  13. 13. Most  prevalent  clause  type:     “These  results  suggest  that...”  Adverb/Connec<ve   thus,  therefore,  together,  recently,  in  summary    Determiner/Pronoun     it,  this,  these,  we/our  Adjec<ve   previous,  future,  beeer  Noun  phrase   data,  report,  study,  result(s);  method  or  reference  Modal   form  of    ‘to  be’,  may,  remain  Adjec<ve   ogen,  recently,  generally  Verb   show,  obtain,  consider,  view,  reveal,  suggest,   hypothesize,  indicate,  believe  Preposi<on     that,  to  
  14. 14. Repor<ng  verbs  vs.  epistemic  value:  Value  =  0   establish,  (remain  to  be)  elucidated,    (unknown)   be  (clear/useful),  (remain  to  be)  examined/determined,   describe,  make  difficult  to  infer,  report  Value  =  1   be  important,  consider,  expect,  hypothesize  (5x),  give  (hypothe<cal)   insight,  raise  possibility  that,  suspect,  think  Value  =  2   appear,  believe,  implicate  (2x),  imply,  indicate  (12x),  play  a  (probable)   role,  represent,  suggest  (18x),  validate  (2x),    Value  =  3   be  able/apparent/important  /posi<ve/visible,  compare  (presumed  true)   (2x),  confirm  (2x),  define,    demonstrate  (15x),  detect  (5x),   discover,  display  (3x),  eliminate,  find  (3x),  iden<fy  (4x),   know,  need,  note  (2x),  observe  (2x),  obtain  (success/ results-­‐  3x),  prove  to  be,  refer,  report(2x),    reveal  (3x),   see(2x),  show(24x),    study,  view  
  15. 15. Finding  Claimed  Knowledge  Updates  [10]:  Defini<on:    1)  A  CKU  expresses  a  proposi<on  about  biological  en<<es    2)  A  CKU  is  a  new  proposi<on  3)  The  authors  present  the  CKU  as  factual:  =>  Strength  =  Certainty  4)  A  CKU  is  derived  from  experimental  work  described  in  the  ar<cle:  =>  Basis  =  Data  5)  The  ownership  is  a9ributed  to  the  author(s)  of  the  ar<cle.    =>  Source  =  Author,  Explicit  3),  4)  and  5)  are  either  explicitly  expressed  or  structurally  conveyed:  Here  we  used  mass  spectrometry  to  iden:fy  HuD  as  a  novel  SMN-­‐ interac;ng  partner  Our  analysis  of  known  HuD-­‐associated  mRNAs  iden:fied  cpg15   mRNA  as  a  highly  abundant  mRNA  in  HuD  Ips  
  16. 16. Automa<c  hedge  detec<on  with   The  Xerox  Incremental  Parser:   Concept-­‐matching:   Match  concept  pa9erns  with  rules   Assign  features  to  keywords,  dependencies  and  sentences       General  linguis<c  analysis  of  running  texts:   Extract  syntac<c  dependencies  between  words   Chunking   Part-­‐of-­‐speech  disambigua<on   Segment  the  sentences  into  words   Segment  the  text  into  sentences  
  17. 17. Result:  CKUs  appear  throughout  the  paper   bio-event   entity 1 event name entity 2 location HuD interaction SMN motor neurons Title Abstract Intro. Results Figures Discussion CitationInteraction of Here we used Here we Together with SMN Our MS and Furthermore,survival of mass identify HuD our co-IP interacts co-IP data these findingsmotor spectrometry as a novel data, these with HuD. demonstrate are consistentneuron to identify interacting results a strong with recent(SMN) and HuD as a partner of indicate that interaction studiesHuD proteins novel SMN, SMN between demonstrating[with m RNA neuronal associates SMN and that thecpg15rescues SMN- with HuD in HuD in interaction ofmotor neuron interacting motor spinal motor HuD with theaxonal partner. neurons. neuron spinaldeficits] axons. muscular atrophy (SMA) protein SMN …
  18. 18. The  Xerox  Incremental  Parser:   Concept-­‐matching:   Match  concept  pa9erns  with  rules   Assign  features  to  keywords,  dependencies  and  sentences       General  linguis<c  analysis  of  running  texts:   Extract  syntac<c  dependencies  between  words   Chunking   Part-­‐of-­‐speech  disambigua<on   Segment  the  sentences  into  words   Segment  the  text  into  sentences  
  19. 19. The  formal  model   ©  Jodi  Schneider,    with  thanks  to  Siggi  Handschuh  
  20. 20. orca  [11]    vocab.deri.ie/orca    
  21. 21. Example  Usage        <claim>  orca:hasBasis  orca:Data  .  
  22. 22. Basis  
  23. 23. Source  
  24. 24. ConfidenceLevel  
  25. 25. How  to  represent  the  hierarchy?   lack  of  knowledge  <  hypothe;cal  knowledge     <  dubita;ve  knowledge  <  doxas;c  knowledge    •  skos:broaderThan  –  not  appropriate  •  skos  Collec<ons  add  an  unwanted  layer  of   complexity.  •  Our  approach:  transi<ve  proper<es   “lessCertain”  and  “moreCertain”  
  26. 26. Transi<ve  proper<es  used  for   ConfidenceLevel  
  27. 27. ConfidenceLevel  &  its  Rela<onships  
  28. 28. Possible  Applica<ons  
  29. 29. Add  knowledge  value/basis/source     to  a  bio-­‐event    Biological  statement    with  epistemic  markup   Epistemic  evalua6on  Our  findings  reveal  that  miR-­‐373  would  be  a   Value  =  Probable  poten<al  oncogene  and  it  par<cipates  in  the   Source  =  Author  carcinogenesis  of  human  esophageal  cancer  by   Basis  =  Data    suppressing  LATS2  expression.        Further  biochemical  characteriza<on  of  hMOBs   Value  =  Presumed  showed  that  only  hMOB1A  and  hMOB1B  interact   true  with  both  LATS1  and  LATS2  in  vitro  and  in  vivo  [39].   Source  =  Reference   Basis  =  Data    Moreover,  the  mechanisms  by  which  tumor   Value  =  Possible  suppressor  genes  are  inhibited  may  vary  between   Source  =  Unknown  tumors.   Basis  =  Unknown  
  30. 30. E.g.  to  augment  Medscan  [13]  Biological  statement  with  Medscan/ MedScan  Analysis:   Epistemic  epistemic  markup   evalua6on  Furthermore,  we  present  evidence  that   IL-­‐6  è  NUCB2  (nesfa;n-­‐1)   Value  =  Probable  the  secre;on  of  nesfa:n-­‐1  into  the   Rela<on:  MolTransport   Source  =  Author  culture  media  was  drama<cally  increased   Effect:  Posi<ve   Basis  =  Data    during  the  differen<a<on  of  3T3-­‐L1   CellType:  Adipocytes    preadipocytes  into  adipocytes  (P  <  0.001)   Cell  Line:  3T3-­‐L1  and  a{er  treatments  with  TNF-­‐alpha,    IL-­‐6,  insulin,  and  dexamethasone  (P  <  0.01).  
  31. 31. Or  Biological  Exchange  Language  [14]:    Biological  statement  with   BEL  representa6on:   Epistemic  BEL/  epistemic  markup   evalua6on  These  miRNAs  neutralize  p53-­‐ Increased  abundance  of  miR-­‐372   Value  =  Possible   decreases:  Increased  ac;vity  of  TP53  mediated  CDK  inhibi;on,   Source  =   decreases  ac;vity  of  CDK  protein  family  possibly  through  direct   r(MIR:miR-­‐372)  -­‐| Unknown  inhibi;on  of  the  expression  of   (tscript(p(HUGO:Trp53))  -­‐|   Basis  =  the  tumor-­‐suppressor  LATS2.     kin(p(PFH:”CDK    Family”)))   Unknown       Increased  abundance  of  miR-­‐372   decreases  abundance  of  LATS2   r(MIR:miR-­‐372)  -­‐|  r(HUGO:LATS2)  
  32. 32. Using  ORCA  for  Nanopublica<ons  [15]:  •  Use  to  indicate  Strength,  Basis,  Source  of   Asser<ons:     Knowledge  Strength,   Methods   Authors,  DOIs   Basis,  Source  
  33. 33. Next  steps:    •  Con<nuing  experiments  with  automated   detec<on  •  Can  be  used  in  Claim-­‐Evidence  network   projects,  e.g.  Data2Seman<cs  or  DIKB  •  Could  replace  more  complicated  models  of   argumenta<on  •  Ontology  is  available  for  all  to  use!    
  34. 34. Thank  you!  •  Funding:     •  Discussion  partners:     –  Elsevier  Labs   –  Phil  Bourne,  UCSD   –  NWO  Casimir  programme   –  Ed  Hovy,    •  Collaborators:     –  Gully  Burns,  ISI   –  Henk  Pander  Maat,  UU   –  Joanne  Luciano,  RPI   –  Agnes  Sandor,  XRCE   –  Tim  Clark  et  al.,  Harvard   –  Siegfried  Handshuh,  DERI   –  Rinke  Hoekstra  &  co,  VU   –  Richard  Boyce  &  co,  UPi9   –  Maria  Liakata,  EBI   –  Sophia  Ananiadou  &  co,   NaCTeM    
  35. 35. Ques<ons?       Anita  de  Waard   a.dewaard@elsevier.com   h9p://elsatglabs.com/labs/anita/       Jodi  Schneider   jodi.schneider@deri.org    h9p://jodischneider.com/jodi.html      
  36. 36. References  [1]  Latour,  B.  and  Woolgar,  S.,  Laboratory  Life:  the  Social  Construc<on  of  Scien<fic  Facts,  1979,  Sage    [2]  Myers,  G.  (1992).  ‘In  this  paper  we  report’:  Speech  acts  and  scien<fic  facts,  Jnl  of  Pragmatlcs  17  (1992)  295-­‐313  [3]  Swales,  J.  (1990).  Genre  Analysis,  English  in  Acad.  and  Res.Se}ngs,  Cambridge  University  Press,  1990.    [4]  Salager-­‐Meyer,  F.  (1994),  Hedges  and  Textual  Communica<ve  Func<on  in  Medical  English  Wri9en  Discourse,  English  for  Specific  Purposes,  Vol.  13,  No.  2,  pp.  149-­‐170,  1994.    [5]  Light  M,  Qiu  XY,  Srinivasan  P.  (2004).  The  language  of  bioscience:  facts,  specula<ons,  and  statements  in  between.  BioLINK  2004:  Linking  Biological  Literature,  Ontologies  and  Databases  2004:17-­‐24.  [6]  Wilbur  WJ,  Rzhetsky  A,  Shatkay  H  (2006).  New  direc<ons  in  biomedical  text  annota<ons:  defini<ons,  guidelines  and  corpus  construc<on.  BMC  Bioinforma<cs  2006,  7:356.  [7]  Thompson  P.,  Venturi  G.  et  al.  (2008).  Categorising  modality  in  biomedical  texts.  Proc.  LREC  2008  Wkshp  Building  and  Evalua<ng  Resources  for  Biomedical  Text  Mining  2008.  [8]  Kim,  S-­‐M.  Hovy,  E.H.  (2004).  Determining  the  Sen<ment  of  Opinions,COLING  conference,  Geneva,  2004.    [9]    de  Waard,  A.  and  Pander  Maat,  H.  (2012).  Epistemic  Modality  and  Knowledge  A9ribu<on  in  Scien<fic  Discourse:  A  Taxonomy  of  Types  and  Overview  of  Features.  Workshop  on  Detec<ng  Structure  in  Scholarly  Discourse,  ACL  2012.    [10]  Sándor,  À.  and  de  Waard,  A.,  (2012).  Iden<fying  Claimed  Knowledge  Updates  in  Biomedical  Research  Ar<cles,  Workshop  on  Detec<ng  Structure  in  Scholarly  Discourse,  ACL  2012.    [11]  de  Waard,  A.  and  Schneider,  J.  (2012)  Formalising  Uncertainty:  An  Ontology  of  Reasoning,  Certainty  and  A9ribu<on  (ORCA),  SATBI+SWIM,  ISWC  2012.    [12]  Medscan  [13]  Biological  Expression  Language  –  h9p://www.openbel.org    [14]  Groth  et  al  (2010)  The  anatomy  of  a  nanopublica<on  Informa<on  Services  &  Use  30:51-­‐6  

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