Semantic mediawiki for pharmaceutical knowledge management open version (final)


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A chapter from our new book on the use of open source software within life science industry. There are 23 chapters covering chemistry, bioinformatics, knowledge management, clinical and economics. A unique insight into practices within major pharma and related organisations. More at

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Semantic mediawiki for pharmaceutical knowledge management open version (final)

  1. 1. Open  Chapter  From  The  Book  “Open  source  software  in  life  science  research”   Edited  by  L  Harland,  and  M  Forster  (Woodhead  Publishing)   ISBN-­‐13:  978  1  907568  97  8     ©  2012  The  Authors     Building  Disease  and  Target  Knowledge  With  Semantic  MediaWiki     Lee  Harland,  Catherine  Marshall,  Ben  Gardner,  Meiping  Chang,  Rich  Head  and  Philip   Verdemato   Abstract   The  efficient  flow  of  both  formal  (stored/archived)  and  tacit  knowledge  (held  in   people’s  heads)  is  critical  in  the  new  era  of  information-­‐powered  pharmaceutical   discovery.  Yet,  one  of  the  major  inhibitors  of  this  is  the  constant  flux  within  the  industry,   driven  by  rapidly  evolving  business  models,  mergers  and  collaborations.  A  continued   stream  of  new  employees  and  external  partners  brings  a  need  to  not  only  manage  the   new  information  they  generate,  but  to  find  and  exploit  existing  company  results  and   reports.  The  ability  to  synthesise  this  vast  information  “substrate”  into  actionable   intelligence  is  crucial  to  industry  productivity,  and  is  therefore  the  subject  of  much   activity  within  IT  departments.  In  parallel,  the  new  “digital  biology”  era,  with  immense   DNA  sequencing  capabilities,  ever-­‐larger  genome  wide  association  studies  and  next   generation  disease/systems  modelling  provides  yet  more  and  more  data  to  find,  analyse   and  exploit.  In  this  chapter  we  look  at  the  contribution  that  the  Semantic  MediaWiki   (SMW)  technology  has  made  to  meeting  the  information  challenges  faced  by  Pfizer.  We   describe  two  use-­‐cases  that  address  different  aspects  of  biomedical  research  covering   target  selection  and  validation,  and  disease  knowledge  mapping.  These  examples   highlight  the  flexibility  of  this  software  and  the  ultimate  benefit  to  the  user.       Keywords:  Semantic  MediaWiki,  drug  target,  knowledge  management,  collaboration,   disease  maps   The  Targetpedia   While  many  debate  the  relative  merits  of  target-­‐driven  drug  discovery  [1],  drug  targets   themselves  remain  a  crucial  pillar  of  pharmaceutical  research.  Thus,  one  of  the  most   basic  needs  in  any  company  is  for  a  drug  target  reference,  an  encyclopaedia  providing   key  facts  concerning  these  important  entities.  In  the  majority  of  cases  “target”  can  of   course,  be  either  a  single  protein  or  some  multimeric  form  such  as  a  complex,  protein-­‐ protein  interaction  or  pharmacological  grouping  such  as  “Calcium  Channels”  [2].  Thus,   our  focus  for  Targetpedia  was  to  include  all  proteins  (whether  or  not  they  were   established  targets)  and  known  multi-­‐protein  targets  from  humans  and  other  key   organisms.  While  there  are  a  number  of  sites  on  the  internet  that  provide  some  form  of   summary  information  in  this  space  (e.g.  Wikipedia  [3],  Ensembl  [4],  GeneCards  [5],   EntrezGene  [6]),  none  are  particularly  tuned  to  presenting  the  information  most   required  for  drug  discovery  users.  Furthermore,  such  systems  do  not  incorporate   historical  company-­‐internal  data,  clearly  something  of  high  value  when  assessing   discovery  projects.  Indeed,  in  large  companies  working  on  50+  new  targets  per  year,  the   ability  to  track  the  target  portfolio  and  associated  assets  (milestones,  end  points,   reagents  etc.)  is  vital.  Targets  are  not  unique  to  one  disease  area  and  access  to   compounds,  clones,  cell-­‐lines  and  reagents  from  existing  projects  can  rapidly  accelerate   new  studies.  More  importantly,  access  to  existing  data  on  safety,  chemical  matter   quality,  pathways/mechanisms  and  biomarkers  can  “make  or  break”  a  new  idea.  While   this  is  often  difficult  for  any  employee,  it  is  particularly  daunting  for  new  colleagues   lacking  the  IT  system  knowledge  and  people  networks  often  required  to  find  this   material.  Thus,  the  justification  for  a  universal  protein/target  portal  at  Pfizer  was  
  2. 2. Open  Chapter  From  The  Book  “Open  source  software  in  life  science  research”   Edited  by  L  Harland,  and  M  Forster  (Woodhead  Publishing)   ISBN-­‐13:  978  1  907568  97  8     ©  2012  The  Authors     substantial,  forming  an  important  component  in  an  overall  research  information   strategy.     Design  Choices   In  2010,  Pfizer  informaticians  sought  to  design  a  replacement  for  legacy  protein/target   information  systems  within  the  company.  Right  from  the  start  it  was  agreed  that  the   next  system  should  not  merely  be  a  cosmetic  update,  but  address  the  future  needs  of  the   evolving  research  organisation.  We  therefore  engaged  in  a  series  of  interviews  across   the  company,  consulting  colleagues  with  a  diverse  set  of  job  functions,  experience  and   grades.  Rather  than  focussing  on  software  tools,  the  interviews  centred  around  user   needs  and  asked  questions  such  as:     What  data  and  information  do  you  personally  use  in  your  target  research?   Where  do  you  obtain  that  data  and  information?   Where,  if  any  place,  do  you  store  annotations  or  comments  about  the  work  you   have  performed  on  targets?   Approximately  how  many  targets  per  year  does  your  Research  Unit  (RU)  have  in   active  research?  How  many  are  new?   What  are  your  pain  points  around  target  selection  and  validation?     Based  on  these  discussions,  we  developed  a  solid  understanding  of  what  the  user   community  required  from  the  new  system  and  the  benefits  that  these  could  bring.   Highlights  included:     Use  of  Internet  Tools:  Most  colleagues  described  regular  use  of  the  Internet  as  a   primary  mechanism  to  gain  information  on  a  potential  target.  Google  and   Wikipedia  searches  were  most  common,  their  speed  and  simplicity  fitting  well   with  a  scientist’s  busy  schedule.  The  users  knew  there  were  other  resources  out   there,  but  were  unclear  as  to  which  were  the  best  for  a  particular  type  of  data,  or   how  to  stay  on  top  of  the  ever-­‐increasing  number  of  relevant  web-­‐sites.     Data  Diversity:  Many  interviewees  could  describe  the  types  of  data  that  they   would  like  to  see  in  the  system.  While  there  were  some  differences  between   scientists  from  different  research  areas  (discussed  later),  many  common  areas   emerged.  Human  genetics  and  genomic  variation,  disease  association,  model   organism  biology,  expression  patterns,  availability  of  small  molecules  and  the   competitor  landscape  were  all  high  on  the  list.  Given  that  we  identified  an  excess   of  40  priority  internal  and  external  systems  in  these  areas,  there  was  clearly  a   need  to  help  organise  these  data  for  Pfizer  scientists.     Accessing  the  Internal  Target  Portfolio:  Many  in  the  survey  admitted   frustration  in  accessing  information  on  targets  the  company  had  previously   studied.  Such  queries  were  possible,  particularly  via  the  corporate  portfolio   management  platforms.  However,  these  were  originally  designed  for  project   management  and  other  business  concerns,  and  could  not  support  more   “molecular”  questions  such  as  “Show  me  all  the  company  targets  in  pathway  X  or   gene  family  Y”.  As  expected,  this  issue  was  particularly  acute  for  new  colleagues   who  had  not  yet  developed  “work  arounds”  to  try  to  compile  this  type  of   information.    
  3. 3. Open  Chapter  From  The  Book  “Open  source  software  in  life  science  research”   Edited  by  L  Harland,  and  M  Forster  (Woodhead  Publishing)   ISBN-­‐13:  978  1  907568  97  8     ©  2012  The  Authors     Capturing  Proteins  “Of  Interest”.  One  of  the  most  interesting  findings  was  the   desire  to  track  proteins  of  interest  to  Pfizer  scientists  but  that  were  not  (yet)   part  of  the  official  research  portfolio.  This  included  emerging  targets  that  project   teams  were  monitoring  closely  as  well  as  proteins  that  were  well  established   within  a  disease,  but  were  not  themselves  deemed  “druggable”.  For  instance,  a   transcription  factor  might  regulate  pathways  crucial  to  two  distinct  therapeutic   areas,  yet  as  the  teams  are  geographically  distributed  they  may  not  realise  a   common  interest.  Indeed,  many  respondents  cited  this  intersection  between   biological  pathways  and  the  “idea  portfolio”  as  an  area  in  need  of  much  more   information  support.     Embracing  of  “Wikis”:  We  also  learned  that  a  number  of  research  units  (RUs)   had  already  experimented  with  their  own  approaches  to  managing  day-­‐to-­‐day   information  on  their  targets  and  projects.  Many  had  independently  arrived  at  the   same  solution  and  implemented  a  wiki  system.  Users  liked  the  simplicity,  ease  of   collaboration  and  familiarity  (given  their  use  of  Wikipedia  and  Pfizer’s  own   internal  wiki,  see  the  chapter  by  Gardner  and  Revell.  This  highlighted  that  many   colleagues  were  comfortable  with  this  type  of  approach  and  encouraged  us  to   develop  a  solution  that  was  in  tune  with  their  thinking.       Annotation:  Responses  concerning  annotation  were  mixed;  there  were  a  range   of  opinions  as  to  whether  the  time  spent  adding  comments  and  links  to   information  pages  was  a  valuable  use  of  a  scientists’  time.  We  also  found  that   users  viewed  existing  annotation  software  as  too  cumbersome,  often  requiring   many  steps  and  logging  in  and  out  of  multiple  systems.  However,  it  was  clear   from  the  RU-­‐wikis  that  there  was  some  appetite  for  community  editing  when   placed  in  the  right  context.  It  became  very  clear  that  if  we  wanted  to  encourage   this  activity  we  needed  to  provide  good  tools  that  were  a  fit  with  the  scientists’   workflow  and  provided  a  tangible  value  to  them.       Building  The  System   Targetpedia  was  intended  to  be  a  “first  stop”  for  target-­‐orientated  information  within   the  company.  As  such,  it  needed  to  present  an  integrated  view  across  a  range  of  internal   and  external  data  sources.  The  business  analysis  provided  a  clear  steer  towards  a  wiki-­‐ based  system  and  we  reviewed  many  of  the  different  platforms  before  deciding  upon  the   Semantic  MediaWiki    (SMW)  framework  [7].  SMW  (which  is  an  extension  of  the   MediaWiki  software  behind  Wikipedia)  was  chosen  for  the  following  reasons:     Agility:  While  our  interviews  suggested  users  would  approve  of  the  wiki   approach,  we  were  keen  to  produce  a  prototype  to  test  this  hypothesis.  SMW   offered  many  of  the  capabilities  we  needed  ‘out  of  the  box’  –  certainly  enough  to   produce  a  working  prototype.  In  addition,  the  knowledge  that  this  same   software  underlies  both  Wikipedia  and  our  internal  corporate  wiki  suggested   that  (should  we  be  successful),  developing  a  production  system  should  be   possible.       Familiarity:  As  the  majority  of  scientists  within  the  company  were  familiar  with   MediaWiki-­‐based  sites,  and  many  of  our  specific  target  customers  had  set  up   their  own  instances,  we  should  not  face  too  high  a  barrier  for  adopting  a  new   system.  
  4. 4. Open  Chapter  From  The  Book  “Open  source  software  in  life  science  research”   Edited  by  L  Harland,  and  M  Forster  (Woodhead  Publishing)   ISBN-­‐13:  978  1  907568  97  8     ©  2012  The  Authors       Extensibility:  While  SMW  had  enough  functionality  to  meet  early  stage   requirements,  we  anticipated  that  eventually  we  would  need  to  extend  the   system.  The  open  codebase  and  modular  design  were  highly  attractive  here,   allowing  our  developers  to  build  new  components  as  required  and  enabling  us   to  respond  to  our  customers  quickly.     Semantic  Capabilities:  A  key  element  of  functionality  was  the  ability  to  provide   summarisation  and  taxonomy-­‐based  views  across  the  proteins    (described  in   detail  below).  This  is  actually  one  of  the  most  powerful  core  capabilities  of  SMW   and  something  not  supported  by  many  of  the  alternatives.  The  feature  is  enabled   by  the  “ASK”  query  language  [8]  which  functions  somewhat  like  SQL  and  can  be   embedded  within  wiki  pages  to  create  dynamic  and  interactive  results  sets.   Data  Sourcing   Using  a  combination  of  user  guidance  and  access  statistics  from  legacy  systems,  we   identified  the  major  content  elements  required  for  the  wiki.  For  version  one  of   Targetpedia,  the  entities  chosen  were:  proteins  and  protein  targets,  species,   indications,  pathways,  biological  function  annotations,  Pfizer  people,  departments,   projects  and  research  units.     For  each  entity  we  then  identified  the  types  and  sources  of  data  the  system  needed  to   hold.  Table  1  provides  an  excerpt  of  this  analysis  for  the  protein/target  entity  type.  In   particular,  we  made  use  of  our  existing  infrastructure  for  text-­‐mining  of  the  biomedical   literature,  Pharmamatrix  (PMx,  [9]).  PMx  works  by  automated,  massive-­‐scale  analysis  of   Medline  and  other  text  sources  to  identify  associations  between  thousands  of   biomedical  entities.  The  results  of  this  mining  provide  a  rich  data  source  to  augment   many  of  the  areas  of  scientific  interest.     Name   Description   Example  Sources   General   Functional   Information   General  textual  summary  of  the  target   Wikipedia  [3]   EntrezGene  [6]   Gene  Ontology  [10]   Chromosomal   Information   Key  facts  on  parent  gene  within  genome   Ensembl  [4]   Disease  Links   Known  disease  associations     Genetic  Association  Database   [11]   OMIM  [12]   Literature  associations  (PMx)     GWAS  data  (e.g.  [13])   Genetics   Polymorphisms  and  mouse  phenotypes   Polyphen  2  [14]   Mouse  Genome  Informatics   [15]   Internal  data   Literature  mining  (PMx)   Drug  Discovery   Competitor  landscape  for  this  target   Multiple  commercial   competitor  databases  [2]   Safety   Safety  issues   Comparative  Toxicogenomics   Database  [16]     Pfizer  safety  review   repository   Literature  mining  (PMx)   Pfizer  Project   Projects  have  we  worked  on  this  target  for   Discovery  and  Development  
  5. 5. Open  Chapter  From  The  Book  “Open  source  software  in  life  science  research”   Edited  by  L  Harland,  and  M  Forster  (Woodhead  Publishing)   ISBN-­‐13:  978  1  907568  97  8     ©  2012  The  Authors     Information   portfolio  databases   Pfizer  screening  registration   database   References   Key  references   Gene  RIFs  [6]   Literature  mining  (PMx)   Pathways   Pathways  is  this  protein  a  member  of   Reactome  [17]   BioCarta  [18]   Commercial  pathway  systems   Table  1:  Protein  information  sources  for  Targetpedia               Loading  Data   Having  identified  the  necessary  data,  a  methodology  for  loading  this  into  the   Targetpedia  was  designed  and  implemented,  schematically  represented  in  Figure  1.         Figure  1:  Data  Loading  Architecture.     As  the  chapter  by  Alquier  describes  the  core  SMW  system  in  detail,  here  we  will   highlight  the  important  Targetpedia-­‐specific  elements  of  the  architecture,  such  as:     Source  Management:  For  external  sources  (public  and  licenced  commercial)  a  a   variety  of  data  replication  and  scheduling  tools  were  used  (including  FTP,   AutoSys  [19]  and  Oracle  materialised  views)  to  manage  regular  updates  from  
  6. 6. Open  Chapter  From  The  Book  “Open  source  software  in  life  science  research”   Edited  by  L  Harland,  and  M  Forster  (Woodhead  Publishing)   ISBN-­‐13:  978  1  907568  97  8     ©  2012  The  Authors     source  into  our  data  warehouse.  Most  data  sets  are  then  indexed  by  and  made   queryable  by  loading  into  Oracle,  Lucene  [20]    or  SRS  [21].  Data  sources  use  a   vast  array  of  different  identifiers  for  biomedical  concepts  such  as  genes,  proteins   and  diseases.  We  used  an  internal  system  (similar  to  systems  such  as  BridgeDb   [22])  to  provide  mappings  between  different  identifiers  for  the  same  entity.   Multi-­‐protein  targets  were  sourced  from  our  previously  described  internal  drug   target  database  [2].  Diseases  were  mapped  to  our  internal  disease  dictionary,   which  is  an  augmented  form  of  the  disease  and  condition  branches  of  MeSH  [23].     Data  Provision:  For  each  source,  queries  required  to  obtain  information  for  the   wiki  were  identified.  In  many  instances  this  took  the  form  of  summaries  and   aggregations  rather  than  simply  extracting  data  “as-­‐is”  (described  below).    Each   query  was  turned  into  a  REST-­‐ful  web-­‐service  via  an  in  house  framework  known   as  BioServices.  This  acts  a  wrapper  around  queries  and  takes  care  of  many   common  functions  such  as  load  balancing,  authentication,  output  formatting  and   metadata  descriptions.    Consequently,  each  query  service  (“QWS”  in  the  figure)   is  a  standardised,  parameterised  end  point  that  provides  results  in  a  variety  of   formats  including  XML  and  JSON.  A  further  advantage  of  this  approach  is  that   these  services  (such  as  “general  information  for  a  protein”,  “ontology  mappings   for  a  protein”)  are  also  available  outside  of  Targetpedia.     Data  Loading:  A  MediaWiki  ‘Bot  [24]  was  developed  to  carry  out  the  loading  of   data  into  Targetpedia.  A  ‘bot  is  simply  a  piece  of  software  that  can  interact  with   the  MediaWiki  system  to  load  large  amounts  of  content  directly  into  the  backend   database.  Each  query  service  was  registered  with  the  ‘bot,  along  with  a   corresponding  wiki  template  which  would  hold  the  actual  data.  Templates  [25]   are  similar  to  classes  within  object  orientated  programming  in  that  they  define   the  fields  (properties)  of  a  complex  piece  of  data.  Critically,  templates  also   contain  a  definition  of  how  the  data  should  be  rendered  in  HTML  (within  the   wiki).  This  approach  separates  the  data  from  the  presentation,  allowing  a   flexible  interface  design  and  multiple  views  across  the  same  object.  With  these   two  critical  pieces  in  place,  the  ‘bot  was  then  able  to  access  the  data  and  load  it   into  the  system  via  the  MediaWiki  API  [26].     Update  Scheduling.  As  different  sources  update  content  at  different  rates,  the   system  was  designed  such  that  administrators  could  refresh  different  elements   of  the  wiki  under  different  schedules.  Furthermore,  as  one  might  often  identify   errors  or  omissions  affecting  only  a  subset  of  entries,  the  loader  was  designed   such  that  any  update  could  be  limited  to  a  list  of  specific  proteins  if  desired.   Thus,  administrators  have  complete  control  over  which  sources  and  entries  can   be  updated  at  any  particular  time.     The  result  of  this  process  was  a  wiki  populated  with  data  covering  the  major  entity   types  in  a  semantically  enriched  format.  Figure  2  shows  an  excerpt  of  the  types  of   properties  and  values  stored  for  the  protein,  Phosphodiesterase  5.      
  7. 7. Open  Chapter  From  The  Book  “Open  source  software  in  life  science  research”   Edited  by  L  Harland,  and  M  Forster  (Woodhead  Publishing)   ISBN-­‐13:  978  1  907568  97  8     ©  2012  The  Authors       Figure  2:  Properties  of  PDE5  stored  semantically  in  the  wiki  
  8. 8. Open  Chapter  From  The  Book  “Open  source  software  in  life  science  research”   Edited  by  L  Harland,  and  M  Forster  (Woodhead  Publishing)   ISBN-­‐13:  978  1  907568  97  8     ©  2012  The  Authors     The  Product  And  User  Experience   The  starting  point  for  most  users  is  the  protein  information  pages.  Figure  3  shows  one   such  example.         Figure  3:  A  protein  page  in  Targetpedia  (confidential  information  masked).     The  protein  pages  have  a  number  of  important  design  features.     Social  Tagging:  At  the  top  of  every  page,  buttons  that  allow  users  to  “tag”  and   annotate  targets  of  interest  (discussed  in  detail  below).     The  InfoBox:  As  commonly  used  within  Wikipedia  pages,  customised  to  display   key  facts,  including  database  identifiers,  synonyms,  pathways  and  Gene  Ontology   processes  in  which  the  protein  participates.     Informative  Table  Of  Contents:  The  default  MediaWiki  table  of  contents  was   replaced  with  a  new  component  that  combines  an  immediate,  high  level   summary  of  critical  information  with  hyperlinks  to  relevant  sections.     Textual  Overview:  Short,  easily  digestible  summaries  around  the  role  and   function  of  this  entity  are  provided.  Some  of  this  content  is  obtained  directly   from  Wikipedia,  an  excellent  source  thanks  to  the  Gene  Wiki  initiative  [27].  For   this,  the  system  only  presents  the  first  1-­‐2  paragraphs  initially;  the  section  can   be  expanded  to  the  full  article  with  a  single  click.  This  is  complemented  with   textual  summaries  from  the  National  Center  for  Biotechnology  Information’s   RefSeq  [28]  database.     The  rest  of  the  page  is  made  up  of  more  detailed  sections  covering  the  areas  described  in   Table  1.  For  example,  Figure  4  shows  the  information  we  display  in  the  competitor  
  9. 9. Open  Chapter  From  The  Book  “Open  source  software  in  life  science  research”   Edited  by  L  Harland,  and  M  Forster  (Woodhead  Publishing)   ISBN-­‐13:  978  1  907568  97  8     ©  2012  The  Authors     intelligence  section  and  demonstrates  how  a  level  of  summarisation  has  been  applied.   Rather  than  presenting  all  of  the  competitor  data,  we  present  some  key  facts  such  as   number  of  known  small  molecules,  maximum  clinical  phase  and  most  common   indications.  Should  the  user  be  sufficiently  interested  to  know  more,  many  of  the   elements  in  the  section  are  hyperlinked  to  a  more  extensive  target  intelligence  system   [2]  which  allows  them  to  analyse  all  of  the  underlying  competitor  data.       Figure  4:  The  Competitor  Intelligence  Section.     One  of  the  most  powerful  features  of  SMW  is  the  ability  to  ‘slice  and  dice’  content  based   on  semantic  properties,  allowing  developers  (and  even  technically-­‐savvy  users)  to   create  additional  views  of  the  information.  For  example,  each  protein  in  the  system  has  a   semantic  property  that  represents  its  position  within  a  global  protein  family  taxonomy   (based  upon  a  Medicinal  Chemistry  view  of  protein  function,  see  [29]).  Figure  5  shows   how  this  property  can  be  exploited  though  an  ASK  query  to  produce  a  view  of  proteins   according  to  their  family  membership,  in  this  case  the  Phosphodiesterase  4  family.  The   confidential  information  has  been  blocked  out,  Pfizer  users  see  a  “dashboard”  for  the   entire  set  of  proteins  and  to  what  extent  they  have  been  investigated  by  the  company.   Critically,  SMW  understands  hierarchies,  so  if  the  user  were  to  view  the  same  page  for   “Phosphodiesterases”  they  would  see  all  such  proteins,  including  the  four  shown  here.   Similar  views  have  been  set  up  for  pathway,  disease  and  Gene  Ontology  functions,   providing  a  powerful  mechanism  for  looking  across  the  data  –  and  all  (almost),  for  free!    
  10. 10. Open  Chapter  From  The  Book  “Open  source  software  in  life  science  research”   Edited  by  L  Harland,  and  M  Forster  (Woodhead  Publishing)   ISBN-­‐13:  978  1  907568  97  8     ©  2012  The  Authors       Figure  5.  A  Protein  Family  View.  Internal  users  would  see  the  table  filled  with  company   data  regarding  progression  of  each  protein  within  drug  discovery  programmes.   Collaboration  In  Targetpedia   One  of  the  major  differences  between  Targetpedia  and  our  legacy  protein/target   information  systems  are  features  that  empower  internal  collaboration.  For  instance,   Pfizer  drug  discovery  project  tracking  codes  are  found  on  all  pages  that  represent   internal  targets,  providing  an  easy  link  to  business  data.  Each  project  code  has  its  own   page  within  Targetpedia,  listing  the  current  status,  milestones  achieved  and,   importantly,  the  people  associated  with  the  research.  The  connection  of  projects  to   scientists  was  made  possible  thanks  to  the  corporate  timesheets  which  all  Pfizer   scientists  complete  each  week,  allocating  their  time  against  specific  project  codes.  By   integrating  this  into  Targetpedia  this  administrative  activity  moves  from  simply  a   management  tool  to  something  that  tangibly  enables  collaboration.  Further  integration   with  departmental  information  systems  provides  lists  of  colleagues  involved  in  the   work,  organised  by  work  area.  This  helps  users  of  Targetpedia  find  not  just  the  people   involved,  but  those  from  say,  the  Pharmacodynamics  or  High-­‐throughput  screening   groups.  We  believe  this  provides  a  major  advance  in  helping  Pfizer  colleagues  find  the   right  person  to  speak  to  regarding  a  project  on  a  target  they  have  become  interested  in.      A  second  collaboration  mechanism  revolves  around  the  concept  of  the  “Idea  Portfolio”.   We  wanted  to  make  it  very  easy  for  users  to  assert  an  interest  in  a  particular  protein.   Similar  to  the  Facebook  “like”  function,  the  first  button  in  the  tagging  bar  (Figure  3)   allows  users  to  “Tag  (this  protein)  as  my  target”  with  a  single  click.  This  makes  it  trivial   for  scientists  to  create  a  portfolio  of  proteins  of  interest  to  them  or  their  research  unit.   An  immediate  benefit  is  access  to  a  range  of  alerting  tools,  providing  email  or  RSS   updates  to  new  updates,  database  entries  or  literature  regarding  their  chosen  proteins.   However,  the  action  of  tagging  targets  creates  a  very  rich  dataset  that  can  be  exploited   to  identify  connections  between  disparate  individuals  in  a  global  organisation.  Interest   in  the  same  target  is  obvious,  but  as  mentioned  above,  algorithms  that  scan  the   connections  to  identify  colleagues  with  interests  in  different  proteins  but  the  same   pathway  present  very  powerful  demonstrations  of  the  value  of  this  social  tagging.    
  11. 11. Open  Chapter  From  The  Book  “Open  source  software  in  life  science  research”   Edited  by  L  Harland,  and  M  Forster  (Woodhead  Publishing)   ISBN-­‐13:  978  1  907568  97  8     ©  2012  The  Authors       Figure  6:  Social  networking  around  targets  and  projects  in  Targetpedia.     Finally,  Targetpedia  is  of  course,  a  wiki,  and  while  much  of  the  above  has  concerned  the     import,  organisation  and  presentation  of  existing  information,  we  wanted  to  also  enable   the  capture  of  pertinent  content  from  our  users.  This  is  in  fact,  more  complex  than  it   might  initially  seem.  As  described  in  the  business  analysis  section,  many  research  units   were  running  their  own  wikis,  annotating  protein  pages  with  specific  project   information.  However,  this  is  somewhat  at  odds  with  a  company  wide  system,  where   different  units  working  on  the  same  target  would  not  want  their  information  to  be   merged.  Furthermore,  pages  containing  large  sections  of  content  that  are  specifically   written  by  or  for  a  very  limited  group  of  people  erodes  the  “encyclopaedia”  vision  for   Targetpedia.  To  address  this,  it  was  decided  that  the  main  protein  pages  would  not  be   editable,  allowing  us  to  retain  the  uniform  structure,  layout  and  content  across  all   entries.  This  also  meant  that  updating  would  be  significantly  simpler,  not  having  to   distinguish  between  user-­‐generated  and  automatically  loaded  content.       To  provide  wiki  functionality,  users  can  (with  a  single  click)  create  new  “child”  pages   that  are  automatically  and  very  clearly  linked  to  the  main  page  for  that  protein.  These   can  be  assigned  with  different  scopes,  such  as  a  user-­‐page  e.g.  “Lee  Harland’s  PDE5   page”,  a  project  page  e.g.  “The  Kinase  Group  Page  For  MAPK2”  and  a  research  unit  page,   e.g.  “  Oncology  p53  programme  page”.    In  this  way,  both  the  encyclopaedia  and  the  RU-­‐ specific  information  capture  aspects  of  Targetpedia  are  possible  in  the  same  system.   Search  results  for  a  protein  will  always  take  a  user  to  a  main  page,  but  all  child  pages  are   clearly  visible  and  accessible  from  this  entry  point.  At  present,  the  template  for  the  sub-­‐ pages  is  quite  open,  allowing  teams  to  build  those  pages  as  they  see  fit,  in  keeping  with   their  work  in  in  their  own  wiki  systems.  Finally,  there  may  be  instances  where  people   wish  to  add  very  short  annotations  –  a  key  paper  or  a  URL  that  points  to  some  useful   information.  For  this,  the  child  page  mechanism  may  be  somewhat  overkill.  Therefore,   we  added  a  fourth  collaborative  function  that  allows  users  to  enter  a  short  (255   character)  message  by  clicking  the  “add  comment”  button  on  the  social  tagging  toolbar.   Crucially,  these  are  not  written  into  the  page  itself,  but  stored  within  the  SMW  system   and  dynamically  included  via  an  embedded  ASK  query.  This  retains  the  simple  update   pattern  for  the  main  page  and  allows  for  modification  to  the  presentation  of  the   comments  in  future  versions.     Lessons  Learned     Targetpedia  has  been  in  use  for  over  6  months  to  very  positive  feedback.  During  this   time  there  has  been  much  organisational  change  for  Pfizer,  reaffirming  the  need  for  a  
  12. 12. Open  Chapter  From  The  Book  “Open  source  software  in  life  science  research”   Edited  by  L  Harland,  and  M  Forster  (Woodhead  Publishing)   ISBN-­‐13:  978  1  907568  97  8     ©  2012  The  Authors     central  repository  of  target  information.  Yet,  in  comparison  with  or  legacy  systems,   Targetpedia  has  changed  direction  in  two  major  areas.  Specifically,  it  shifts  from  a  “give   me  everything  in  one  go”  philosophy  to  “give  me  a  summary  and  pointers  where  to  go   next”.  Additionally,  it  addresses  the  increasing  need  for  social  networking,  particularly   through  shared  scientific  interest  in  the  molecular  targets  themselves.     Developing  with  SMW  was  generally  a  positive  experience,  so  much  so  we  went  on  to   reuse  components  in  a  second  project  described  below.  Templating  in  particular  is  very   powerful,  as  are  the  semantic  capabilities  that  make  this  system  unique  within  its   domain.    Performance  (in  terms  of  rendering  the  pages)  was  never  an  issue  although  we   did  take  great  care  to  optimise  the  semantic  ASK  queries  by  limiting  the  number  of  joins   across  different  objects  in  the  wiki.  However,  the  speed  by  which  content  could  be   imported  into  the  system  was  something  that  was  sub-­‐optimal.  There  are  a  considerable   amount  of  data  for  over  20,000  proteins,  as  well  as  people,  diseases,  projects  and  other   entities.  Loading  all  of  this  via  the  MediaWiki  API  took  days  to  perform  a  complete   refresh.  As  the  API  performs  a  number  of  operations  in  addition  to  loading  the  MySQL   database,  we  could  not  simply  bypass  it  and  insert  content  directly  into  the  database   itself.  Therefore,  alternatives  to  the  current  loading  system  may  have  to  be  found  for  the   longer  term,  something  that  will  involve  detailed  analysis  of  the  current  API.  Yet,  even   with  this  issue,  we  were  able  to  provide  updates  along  a  very  good  timeline  that  was   acceptable  to  the  user  community.  A  second  area  of  difficulty  was  correctly  configuring   the  system  for  text  searches.  The  MediaWiki  search  engine  is  quite  particular  in  how  it   searches  the  system  and  displays  the  results,  which  forced  us  to  alter  the  names  of  many   pages  so  search  results  returned  titles  meaningful  to  the  user.       Vocabulary  issues  represented  another  major  hurdle,  and  while  not  the  “fault”  of  SMW,   they  did  hinder  the  project.  For  instance,  there  are  a  variety  of  sources  that  map  targets   to  indications  (OMIM,  internal  project  data,  competitor  intelligence),  yet  each  uses  a   different  disease  dictionary.  Thus,  users  wish  to  click  on  ‘asthma’  to  see  all  associated   proteins  and  targets,  but  this  is  quite  difficult  to  achieve  without  much  laborious   mapping  of  disease  identifiers  and  terms.  Conversely,  there  are  no  publically  available   standards  around  multi-­‐protein  drug  targets  (e.g.  “Protein  Kinase  C”,  “GABA  Receptor”,   “Gastric  Proton  Pump”).  This  means  that  public  data  regarding  these  entities  is  poorly   organised  and  difficult  to  identify  and  integrate.  As  a  consequence,  we  are  able  to   present  only  a  small  amount  of  information  for  these  entities,  mostly  driven  from   internal  systems  (we  do  of  course  provide  links  to  the  pages  for  the  individual  protein   components).  We  believe  target  information  provision  would  be  much  enhanced  if  there   were  open  standards  and  identifiers  available  for  these  entities.  Indeed,  addressing   vocabulary  and  identity  challenges  may  be  key  to  progressing  information  integration   and  appear  to  be  good  candidates  for  precompetitive  collaboration  [30].     For  the  future,  we  would  like  to  create  research  unit-­‐specific  sections  within  the  pages,   sourced  from  (e.g.)  disease-­‐specific  databases  and  configured  to  appear  to  relevant   users  only.  We  are  also  investigating  the  integration  of  “live”  data  by  consuming  web-­‐ services  directly  into  the  wiki  using  available  extensions  [31].  We  conclude  that  SMW   provides  a  powerful  platform  from  which  to  deliver  this  and  other  enhancements  for   our  users.  
  13. 13. Open  Chapter  From  The  Book  “Open  source  software  in  life  science  research”   Edited  by  L  Harland,  and  M  Forster  (Woodhead  Publishing)   ISBN-­‐13:  978  1  907568  97  8     ©  2012  The  Authors     The  Disease  Knowledge  Workbench  (DKWB)   The  Pfizer  Indications  Discovery  Research  Unit  (IDU)  was  established  to  identify  and   explore  alternative  indications  for  compounds  that  have  reached  late  stage  clinical   development.  The  IDU  is  a  highly  collaborative  group;  project  leads,  academic   collaborators  and  computational  scientists  work  together  to  develop  holistic  views  of   the  cellular  processes  and  molecular  pathways  within  a  disease  of  interest.  Such  deep   understanding  allows  the  unit  to  address  key  areas  such  as  patient  stratification,   confidence  in  target  rationale,  and  identification  of  the  best  therapeutic  mechanism  and   outcome  biomarkers.  Thus,  fulfilling  the  IDUs’  mission  requires  the  effective   management  and  exploitation  of  relevant  information  across  a  very  diverse  range  of   diseases.     The  information  assessed  by  the  IDU  comes  from  a  range  of  internal  and  external   sources,  covering  both  raw  experimental  output  and  higher-­‐level  information  such  as   the  biomedical  literature.  Interestingly,  the  major  informatics  gap  was  not  the   management  and  mining  of  these  data  per  se,  as  these  functions  were  already  well   provided  for  with  internal  and  public  databases  and  tools.  Rather,  the  group  needed  a   mechanism  to  bring  together  and  disseminate  the  knowledge  that  they  had  assessed  and   synthesised,  essentially  detailing  their  hypotheses  and  the  data  that  led  them  there.  As   one  would  expect,  the  scientists  used  lab  noted  books  to  record  experiments,  data   mining  tools  to  analyse  data  and  read  many  scientific  papers.  However,  like  many   organisations,  the  only  place  in  which  each  ‘story’  was  brought  together  was  in  a  slide   deck,  driven  by  the  need  to  present  coherent  arguments  to  the  group  members  and   management.    Of  course,  this  is  not  optimal  with  these  files  (including  revisions  and   variants)  quickly  becoming  scattered  through  hard  drives  and  document  management   systems.  Furthermore,  they  often  lack  clear  links  to  data  supporting  conclusions  and   have  no  capability  to  link  shared  biology  across  different  projects.  This  latter  point  can   be  especially  critical  in  a  group  running  a  number  of  concurrent  investigations;  staying   abreast  of  the  major  elements  of  each  project  and  their  interdependencies  can  be   difficult.  Therefore,  the  IDU  and  the  computational  sciences  group  began  an  experiment   to  develop  tools  to  move  disease  knowledge  management  away  from  slides  and  into  a   more  fit  for  purpose  environment.   Design  Choices   Any  piece  of  software  that  allows  users  to  enter  content  could  fall  into  the  category  of   “knowledge  management”.  However,  there  are  a  number  of  tools  that  allow  users  to   represent  coherent  stories  in  an  electronic  representation.  These  range  from  notebook-­‐ style  applications  [32]  to  general  mind-­‐mapping  software  (e.g.  [33])  and  more   specialised  variants  such  as  the  Compendium  platform  for  idea  management  [34].  A   particularly  relevant  example  is  the  I2  Analysts  Notebook  [35],  an  application  used   throughout  law  enforcement,  intelligence  and  insurance  agencies  to  represent  complex   stories  in  semi-­‐graphical  form.  While  we  had  previously  explored  this  for  knowledge   representation  [2]  and  were  impressed  with  its  usability  and  relationship  management,   its  lack  of  “tuning”  to  the  biomedical  domain  was  a  major  limiting  factor.       A  further  type  of  system  considered  was  one  very  tuned  to  disease  modelling,  such  as   the  Biological  Expression  Language  (BEL)  Framework  produced  by  Selventa  [36].  This   approach  describes  individual  molecular  entities  in  a  causal  network,  allowing  
  14. 14. Open  Chapter  From  The  Book  “Open  source  software  in  life  science  research”   Edited  by  L  Harland,  and  M  Forster  (Woodhead  Publishing)   ISBN-­‐13:  978  1  907568  97  8     ©  2012  The  Authors     computer  modelling  and  simulation  to  be  performed  to  understand  the  effects  of  any   form  of  perturbation.  While  this  is  an  important  technology,  it  works  at  a  very  different   level  to  the  needs  of  the  IDU.  Indeed,  our  aim  was  not  to  try  to  create  a  mathematical  or   causal  model  of  the  disease,  but  to  provide  somewhere  for  the  scientists  interpretation   of  these  and  other  evidence  to  reside.  This  interpretation  takes  the  form  of  a  “report”   comprised  of  free  text,  figures/images,  tables,  text  and  hyperlinks  to  files  of  underlying   evidence.  This  is  of  course,  very  wiki-­‐like  and  the  system  which  most  matched  these   needs  was  AlzSWAN  [37],  a  community-­‐driven  knowledge  base  of  Alzheimer  disease   hypotheses.  AlzSWAN  is  a  wiki-­‐based  platform  that  allows  researchers  to  propose,   annotate,  support  and  refute  particular  pathological  mechanisms.  Users  attach   “statements”  to  a  hypothesis  (based  on  literature,  experimental  data  or  their  own  ideas),   marking  whether  these  are  consistent  with  the  hypothesis.  This  mechanism  to  present,   organise  and  discuss  the  pathophysiological  elements  of  the  disease  was  very  aligned   with  the  needs  of  our  project,  capturing  at  the  level  of  scientific  discourse,  rather  than   individual  molecular  networks.     Building  The  System   While  AlzSWAN  came  closest  to  our  desired  system,  it  has  a  top-­‐down  approach  to   knowledge  management;  one  provides  a  hypothesis  and  then  provides  the  evidence  for   and  against  it.  In  the  case  of  the  IDU,  the  opposite  was  required,  namely  the  ability  to   manage  individual  findings  and  ideas  with  the  ultimate  aim  of  brining  these  together   within  a  final  therapeutic  strategy.  Thus,  we  concluded  that  a  different  knowledge   structure  than  AlzSWAN  was  needed,  but  still  took  great  inspiration  from  that  tool.   Given  that  the  concept  of  the  disease  knowledge  workbench  was  very  much  exploratory,   a  way  to  develop  a  prototype  at  minimal  cost  was  essential.  It  was  here  that  experience   with  SMW  within  Targetpedia  enabled  the  group  to  move  rapidly  and  create  an   AlzSWAN-­‐like  system,  tailored  to  the  IDU’s  needs,  within  a  matter  of  weeks.  Without  the   ready  availability  of  a  technology  that  could  provide  the  basis  for  the  system,  it  is   unlikely  the  idea  of  building  the  workbench  would  have  turned  into  a  reality.     Modelling  Information  In  DKWB   Informaticians  and  IDU  scientists  collaborated  in  developing  the  information  model   required  to  correctly  manage  disease  information  in  the  workbench.  The  principle  need   was  to  take  a  condition  such  as  sepsis  and  divide  it  into  individual  physiological   components,  as  shown  in  Figure  7.        
  15. 15. Open  Chapter  From  The  Book  “Open  source  software  in  life  science  research”   Edited  by  L  Harland,  and  M  Forster  (Woodhead  Publishing)   ISBN-­‐13:  978  1  907568  97  8     ©  2012  The  Authors       Figure  7.  Dividing  sepsis  into  physiological  sub-­‐components.  Dashed  grey  boxes   surround  components  that  are  hypothesised  and  not  yet  validated  by  the  team.     Each  component  in  Figure  7  represents  an  “assertion”,  something  that  describes  a   specific  piece  of  biology  within  the  overall  context  of  the  disease.  As  can  be  seen,  these   are  not  simple  binary  statements,  but  represent  more  complex  scientific  phenomena,  an   example  being  “increases  in  levels  of  circulating  T-­‐Lymphocytes  are  primarily  the  result  of   the  inflammatory  cascade”.  There  are  multiple  levels  of  resolution  and  assertions  can  be   parents  or  children  of  other  assertions.  For  instance,  assertions  can  be  observations   (biology  with  supporting  experimental  or  published  data)  or  hypotheses  (predicted  but   not  yet  experimentally  supported).  It  should  be  noted  that  just  because  observations   were  the  product  of  published  experiments  that  does  not  necessarily  mean  that  are   treated  as  fact.  Indeed,  one  of  the  primary  uses  for  the  system  was  to  critique  published   experiments  and  store  the  teams’  view  of  whether  that  data  could  be  trusted.   Regardless,  each  can  be  associated  with  a  set  of  properties  covering  its  nature  and  role   within  the  system,  as  described  in  Table  2.  Properties  such  as  the  evidence  level  are   designed  to  allow  filtering  between  clinical  and  known  disease  biology  and  more   unreliable  data  from  pre-­‐clinical  studies.     Name   Description   Example  Values   Semantically   Indexed   Type   Describes  the  nature  of  the   assertion   Hypothesis  or  Observation   Y   Role   The  type  of  disease  biology  the   assertion  concerns   Disease  Mechanism;  Biomarker;   Therapeutic  Intervention;   Patient  Stratification   Y   Outcome   The  ultimate  effect  of  the  assertion   on  disease  pathology  (where   relevant)   Known  Negative†;  Predicted   Negative;  Known  Positive;   Predicted  Positive;  Unknown   Y   Evidence   Level   The  clinical  relevance  of  the   assertion   Established  Disease  Biology;   Clinical  Finding;     Pre-­‐clinical  Finding   Y   Parent   The  parent  assertion     Y   Status   Current  status  of  the  curation   Not  started   Y  
  16. 16. Open  Chapter  From  The  Book  “Open  source  software  in  life  science  research”   Edited  by  L  Harland,  and  M  Forster  (Woodhead  Publishing)   ISBN-­‐13:  978  1  907568  97  8     ©  2012  The  Authors     around  this  assertion   In  progress   Under  review   Complete   Informative   Text   Free  text  entry  of  synopsis,  figures,   and  other  pertinent  information   Free  text  and  image  entry   N   Semantic   Tags   Tag  entry  with  key  entities  of   interest   Genes,  Cells,  Diseases  etc.   Y   References     Literature  References     Y   †  in  this  context,  “negative”  means  making  the  disease  or  symptoms  worse   Table  2:  The  composition  of  an  assertion.     While  we  did  not  want  to  force  scientists  to  encode  extensive  complex  interpretations   into  a  structured  form,  there  was  a  need  to  identify  and  capture  the  important   components  of  any  assertion.  Thus,  a  second  class  of  concepts  within  the  workbench   were  created,  known  as  “objects”.  An  object  represents  a  single  biomedical  entity  such   as  a  gene,  protein,  target,  drug,  disease,  cellular  process  and  so  on.  Software  developed   on  the  Targetpedia  project  allowed  us  to  load  sets  of  known  entities  from  source   databases  and  vocabularies  into  the  system,  in  bulk.  By  themselves,  these  objects  are   “inert”,  but  they  come  alive  when  semantically  tied  to  assertions.  For  instance,  for  the   hypothesis  “Altered  Monocyte  Cytokine  Release  Contributes  to  Immunosuppression  in   Sepsis”,  one  can  attach  the  entities  involved  such  as  Monocyte  cells  and  specific  cytokine   proteins.  This  “semantic  tagging”  builds  up  comprehensive  relationships  between   pathophysiology  and  the  cellular  and  molecular  components  that  underlie  these  effects.   They  also  enable  the  identification  of  shared  biology.  For  instance,  the  wiki  can  store  all   known  molecular  pathways  as  observations;  assigning  all  of  the  protein  members  to  it   via  semantic  tagging.  Then,  as  authors  of  other  assertions  tag  their  pages  with  the   principle  proteins  involved,  an  ASK  query  can  automatically  identify  relevant  pathways   and  present  them  on  the  page  without  any  need  for  user  intervention.  This  can  be   extended  to  show  all  assertions  in  the  system  that  are  semantically  tagged  with  proteins   in  any  shared  pathway,  regardless  of  what  disease  areas  they  describe.  This  aids  the   identification  of  common  pathways  and  potential  synergies  across  projects.       The  Product  and  User  Experience   To  allow  Pfizer  scientists  to  fully  describe  and  present  their  hypotheses  and  summaries,     the  assertions  within  the  system  are  a  mix  of  structured  data  (Table  2,  semantic  tagging)   and  free-­‐form  text  and  images.  The  semantic  forms  extension  [38]    provided  a  powerful   and  elegant  mechanism  to  structure  this  data  entry.  Figure  8  shows  how  an  assertion  is   created  or  edited.    
  17. 17. Open  Chapter  From  The  Book  “Open  source  software  in  life  science  research”   Edited  by  L  Harland,  and  M  Forster  (Woodhead  Publishing)   ISBN-­‐13:  978  1  907568  97  8     ©  2012  The  Authors         Figure  8.  The  semantic  form  for  creating  a  new  assertion.  Note,  the  free-­‐text  entry   area  (under  “Main  Document  Text”)  has  been  truncated  for  the  figure.     The  top  of  the  edit  form  contains  a  number  of  drop-­‐down  menus  (dynamically  built   from  vocabularies  in  the  system)  to  enable  users  to  rapidly  set  the  main  properties  for   an  assertion.  Below  this  is  the  main  wiki-­‐text  editing  area,  using  the  FCKEditor  plugin   that  enables  users  to  work  in  a  word-­‐processor  like  mode  without  needing  to  learn  the   wiki  markup  language.  Next  is  the  semantic  tagging  area,  where  assertions  can  be   associated  with  the  objects  of  interest.  Here,  the  excellent  auto-­‐complete  mechanism   that  suggests  as  one  types  is  employed,  allowing  the  user  to  more  easily  enter  the   correct  terms  from  supported  vocabularies.  The  user  must  enter  the  nature  of  the   association  (“Link”  on  the  figure),  chosen  from  a  drop-­‐down  menu.    One  of  the  most   common  links  is  “Has  Actor”,  meaning  that  the  assertion  is  associated  with  a  molecular   entity  that  plays  an  active  role  within  it.  Finally,  additional  figures  and  references  can  be   added  using  the  controls  at  the  bottom  of  the  page.       Once  the  user  has  finished  editing,  the  page  is  rendered  into  a  user-­‐friendly  viewing   format  via  a  wiki  template,  as  shown  in  Figure  9a.  As  can  be  seen  in  Figure  9b,  the   system  renders  semantic  tags  (in  this  case  showing  that  CD4+  T-­‐Cells  have  been   associated  with  the  assertion).  In  addition,  via  an  ASK  query,  connections  to  other  
  18. 18. Open  Chapter  From  The  Book  “Open  source  software  in  life  science  research”   Edited  by  L  Harland,  and  M  Forster  (Woodhead  Publishing)   ISBN-­‐13:  978  1  907568  97  8     ©  2012  The  Authors     assertions  in  the  system  are  identified  and  dynamically  inserted  these  at  the  bottom  of   the  page,  in  this  case  identifying  two  child  observations  that  already  exist  in  the  system.       (a)       (b)       Figure  9:  (a)  An  assertion  page  as  seen  after  editing.  (b)  A  semantic  tag  and   automatic  identification  of  related  assertions       All  assertions  are  created  as  part  of  a  ‘project’,  normally  focused  on  a  disease  of  interest.   In  some  cases  disease  knowledge  was  retrospectively  entered  into  the  system  from   legacy  documents  and  notes  and  subsequently  further  expanded.  New  projects  were   initiated  directly  in  the  wiki,  following  discussions  on  the  major  elements  of  the  disease   by  the  scientist.  From  the  main  project  page  (Figure  10),  one  can  get  an  overview  of  the   disease,  its  pathogenesis  and  progression,  diagnosis  and  prevalence,  standard  care  and   market  potential,  as  well  as  key  disease  mechanisms.  The  assertions  listed  at  the  bottom  
  19. 19. Open  Chapter  From  The  Book  “Open  source  software  in  life  science  research”   Edited  by  L  Harland,  and  M  Forster  (Woodhead  Publishing)   ISBN-­‐13:  978  1  907568  97  8     ©  2012  The  Authors     of  this  page  (Figure  10b)  is  divided  into  three  sections:  (i)  “top  level  disease  mechanism”   (i.e.  those  directly  linked  to  the  page),  (ii)  “all  other  disease  mechanisms  below  the  top   level”  (i.e.  those  which  are  children  of  parent  associations  in  (i))  and  (iii)  “non-­‐disease   mechanism  assertions  such  as  target/pathway  or  therapeutic  intervention”.  One  can   drill  into  one  of  the  assertion  pages  and  from  there  navigate  back  to  the  project  main   page  or  any  parent  or  child  assertion  pages  using  the  semantic  network  hyperlinks.  In   this  way  the  workbench  is  not  only  useful  as  a  way  to  capture  disease  knowledge  but   also  provides  a  platform  to  present  learning  on  diseases  and  pathways  to  project  teams   across  the  company.  As  individual  targets  and  diseases  are  semantically  indexed  on  the   pages,  one  can  explore  the  knowledge  base  in  many  ways  and  move  freely  from  one   project  to  another.     a.        
  20. 20. Open  Chapter  From  The  Book  “Open  source  software  in  life  science  research”   Edited  by  L  Harland,  and  M  Forster  (Woodhead  Publishing)   ISBN-­‐13:  978  1  907568  97  8     ©  2012  The  Authors     b.       Figure  10:  The  sepsis  project  page.  The  upper  portion  (a)  is  generated  by  the  user.  The   lower  portion  (b)  is  created  automatically  by  the  system.     In  summary,  we  have  developed  a  knowledge  system  that  allows  scientist  to   collaboratively  build  and  share  disease  knowledge  and  interpretation  with  a  low  barrier   to  adoption.  Much  of  the  data  capture  process  is  deliberately  manual  –  aiming  to  capture   what  the  scientist  thinks,  rather  than  what  a  database  knows.  However,  future  iterations   of  the  tool  could  benefit  from  more  intelligent  and  efficient  forms  of  data  entry,   annotation  and  discovery.  Better  connectivity  to  internal  databases  would  be  a  key  next   step  –  as  assertions  are  entered,  they  could  be  scanned  and  connected  to  important   results  in  corporate  systems.  Automatic  semantic  tagging  would  be  another  area  for   improvement,  the  system  recognising  entities  as  the  text  is  typed  and  presenting  the  list   of  “discovered”  concepts  to  the  user  who  simply  checks  it  is  correct.  Finally,  we  envisage   a  connection  with  tools  such  as  Utopia  (see  the  chapter  by  Pettifer  and  colleagues),  that   would  allow  scientist  to  augment  their  assertions  by  sending  quotes  and  comments  on   scientific  papers  directly  from  the  PDF  reader  to  the  wiki.     Lessons  Learned   The  prototype  disease  knowledge  workbench  brings  a  new  capability  to  our  biologists,   enabling  the  capture  of  their  understanding  and  interpretation  of  disease  mechanisms.  
  21. 21. Open  Chapter  From  The  Book  “Open  source  software  in  life  science  research”   Edited  by  L  Harland,  and  M  Forster  (Woodhead  Publishing)   ISBN-­‐13:  978  1  907568  97  8     ©  2012  The  Authors     However,  there  are  definitely  areas  for  improvement.  In  particular,  while  the  solution   was  based  around  the  writing  of  report-­‐style  content,  the  MediaWiki  software  lacks  a   robust,  intuitive  interface  for  general  editing.  The  FCKeditor  component  provides  some   basic  functionality,  but  it  is  still  feels  as  if  it  is  not  fully  integrated  and  lacks  many   common  features.  Incorporation  of  images  and  other  files  needs  to  evolve  to  a  “drag  and   drop”  method,  rather  than  the  convoluted,  multi-­‐step  process  used  currently.  While  we   are  aware  of  the  Semantic  MediaWiki  Plus  extensions  in  these  areas  [39],  in  our  hands   they  were  not  intuitive  to  use  and  added  too  much  complexity  to  the  interface.  Thus,  the   development  of  components  to  make  editing  content  much  more  user-­‐friendly  should  be   a  high  priority  and  one  which,  thanks  to  the  open-­‐source  nature,  could  be  undertaken  by   ourselves  and  donated  back  to  the  community.     As  for  the  general  concept,  the  disease  knowledge  capture  has  many  benefits  and  aids   organisations  in  capture,  retaining  and  (re-­‐)finding  the  information  surrounding  key   project  decisions.  However,  a  key  question  is,  will  scientists  invest  the  necessary  time  to   enter  their  knowledge  into  the  system?    Our  belief  is  that  scientists  are  willing  to  do  this,   when  they  see  tangible  value  from  such  approaches.  This  requires  systems  to  be  built   that  combine  highly  user-­‐orientated  interfaces  with  intelligent  semantics.  While  we  are   not  there  yet,  software  such  as  SMW  provides  a  tantalising  glimpse  into  what  could  be  in   the  future.  Yet,  as  knowledge  management  technologies  develop  there  is  still  a  need  to   look  at  the  cultural  aspects  of  organisations  and  better  mechanisms  to  promote  and   reward  these  valuable  activities.  Otherwise,  organisations  like  Pfizer  will  be  doomed  to  a   life  of  representing  knowledge  in  slides  and  documents,  lacking  the  provenance   required  to  trace  critical  statements  and  decisions.     Conclusion   We  have  deployed  Semantic  MediaWiki  for  two  contrasting  use  cases.  Targetpedia  is  a   system  that  combines  large-­‐scale  data  integration  with  social  networking  while  the   Disease  Knowledge  Workbench  seeks  to  capture  discourse  and  interpretation  into  a   more  tangible  form.  In  both  cases,  SMW  allowed  us  to  accelerate  the  delivery  of   prototypes  to  test  these  new  mechanisms  for  information  management  and  converting   tacit  knowledge  into  explicit,  searchable  facts.  This  is  undeniably  important  –  as   research  moves  into  a  new  era  where  “data  is  king”,  companies  must  identify  new  and   better  ways  of  managing  those  data.  Yet,  developers  may  not  always  know  the   requirements  up  front,  and  initial  systems  always  require  tuning  once  they  are  released   into  real-­‐world  use.  Software  that  enables  informatics  to  work  with  the  science  and  not   against  it  is  crucial,  and  something  that  SMW  arguably  demonstrates  perfectly.   Furthermore,  as  described  by  Alquier  in  this  book,  SMW  is  geared  to  the  semantic  web,   undoubtedly  a  major  component  of  future  information  and  knowledge  management.   SMW  is  not  perfect,  particularly  in  the  area  of  GUI  interaction,  and  like  any  open  source   technology  some  of  the  extensions  were  not  well  documented  or  had  incompatibilities.   However,  it  stands  out  as  a  very  impressive  piece  of  software  and  one  that  has   significantly  enhanced  our  information  environment.   Acknowledgements   We  thank  members  of  the  IDU,  eBiology,  Computational  Sciences  CoE  and  Research   informatics  for  valuable  contributions  and  feedback.  In  particular,  Christoph  brockel,   Enoch  Huang,  Cory  Brouwer,  Ravi  Nori,  Bryn-­‐Williams-­‐Jones,  Eric  Fauman,  Phoebe  
  22. 22. Open  Chapter  From  The  Book  “Open  source  software  in  life  science  research”   Edited  by  L  Harland,  and  M  Forster  (Woodhead  Publishing)   ISBN-­‐13:  978  1  907568  97  8     ©  2012  The  Authors     Roberts,  Robert  Hernandez,  Markella  Skempri.  We  would  like  to  specifically   acknowledge  Michael  Berry  and  Milena  Skwierawska  who  developed  much  of  the   original  Targetpedia  codebase.  We  thank  David  Burrows  &  Nigel  Wilkinson  for  data   management  and  assistance  in  preparing  the  manuscript.      References     1.  Swinney  D,  How  were  new  medicines  discovered?  Nat  Rev  Drug  Discov  2011.   2.  Harland  L  &  Gaulton  A,  Drug  target  central.  Expert  Opinion  on  Drug  Discovery  2009,   4:857–872.   3.  Wikipedia  [].   4.  Ensembl  [].   5.  GeneCards  [].   6.  EntrezGene  [].   7.  Semantic  MediaWiki  [http://www.semantic-­‐].   8.  Semantic  MediaWiki  Inline-­‐Queries  [http://semantic-­‐].   9.  Hopkins  et  al.,  System  And  Method  For  The  Computer-­‐Assisted  Identification  Of   Drugs  And  Indications.  US  2005/0060305     10.  Harris  MA,  et  al.,  Gene  Ontology  Consortium:  The  Gene  Ontology  (GO)  database   and  informatics  resource.  Nucleic  Acids  Research  2004,  32:D258–61.   11.  Genetic  Association  Database  [].   12.  Online  Mendelian  Inheritance  In  Man  (OMIM)   [].   13.  Collins  F,  Reengineering  Translational  Science:  The  Time  Is  Right.  Science   Translational  Medicine  2011.   14.  PolyPhen-­‐2  [].   15.  Mouse  Genome  Informatics  [].   16.  Comparative  Toxicogenomics  Database  [].   17.  Reactome  [].   18.  Biocarta  [].   19.  AutoSys  [­‐autosys-­‐workld-­‐autom-­‐
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