Costa, Hugo

Rocha, Miguel

Universidade do Minho: RepositoriUM

@Note2	  –	  open-­‐source	  computational	  tools	  for	  biomedical	  text	  mining	  Hugo	  Costa1	  and	  Miguel	  Rocha2	  Emails:	  hcosta@silicolife.com,	  mrocha@di.uminho.pt	  1SilicoLife,	  Braga,	  Portugal	  2Centre	  of	  Biological	  Engineering	  (CEB),	  University	  of	  Minho,	  Braga,	  Portugal	  	  	  The	   @Note2	   project	   (http://www.anote-­‐project.org)	   has	   been	   providing,	   over	  the	   last	   years,	   a	   number	   of	   user-­‐friendly	   open-­‐source	   computational	   tools	  addressing	   the	   main	   tasks	   in	   biomedical	   text	   mining	   (BTM).	   It	   is	   a	   project	  developed	  as	  a	  collaboration	  of	  the	  Biosystems	  group,	  at	  the	  Center	  of	  Biological	  Engineering	  from	  the	  University	  of	  Minho	  (www.ceb.uminho.pt/biosystems)	  and	  the	   company	   SilicoLife	   (www.silicolife.com),	   whose	   first	   version	   has	   been	  published	  in	  2009	  [1].	  Since	   then,	   a	  major	   reformulation	  has	   been	  developed,	   including	   several	   novel	  features	   and	   the	   redesign	   in	   many	   others,	   leading	   to	   the	   current	   version	   2.0,	  recently	  released,	  and	  fully	  written	  in	  Java,	  using	  the	  MySQL	  database.	  	  @Note2	  provides	  an	  end-­‐user	  application,	  supported	  over	  a	  set	  of	  core	  libraries	  with	  a	  well-­‐defined	  API,	  covering	  the	  main	  tasks	  in	  BTM	  related	  to	  Information	  Retrieval	   (IR)	   and	   Information	   Extraction	   (IE).	   IR	  main	   functionalities	   include	  loading	   documents	   from	   local	   folders,	   performing	   queries	   over	   Pubmed	   and	  other	   resources	   (e.g.	   patent	   or	   publisher	   specific	   databases),	   managing	   and	  updating	   query	   results,	   downloading	   the	   relevant	   documents,	   PDF	   to	   text	  conversion,	  relevance	  assignment	  and	  corpora	  management.	  Regarding	  IE,	  the	  main	  tasks	  implemented	  are	  Named	  Entity	  Recognition	  (NER),	  Relationship	   Extraction	   (RE)	   and	   document	   clustering,	   being	   available	   several	  methods	  for	  all	  cases,	  including	  in-­‐house	  and	  third-­‐party	  algorithms.	  @Note	  also	  provides	   user-­‐friendly	   interfaces	   for	   the	   creation	   of	   lexical	   resources	   (e.g.	  dictionaries)	  and	  a	  powerful	  curation	  environment	  for	  NER	  and	  RE	  annotations.	  @Note	   has	   been	   developed	   following	   the	   Model-­‐View-­‐Controller	   (MVC)	  paradigm,	   taking	   advantage	   on	   the	   features	   of	   the	   plug-­‐in	   based	   AI	   Bench	  framework	  (http://www.aibench.org),	  which	  provides	  a	  clear	  separation	  of	  core	  libraries	  and	  GUI	  tools,	  facilitating	  the	  development	  of	  new	  features	  in	  the	  form	  of	  plug-­‐ins	  and	  thus	  inviting	  the	  participation	  of	  the	  community.	  Current	   new	  developments	   include	   a	   novel	  machine	   learning	   (ML)	  workbench	  for	  NER	   and	  RE,	   to	   allow	   training	   and	   validation	   of	  ML	  models	   from	  manually	  annotated	  corpora,	  as	  well	  as	  improved	  multiuser	  support.	  	  	  [1]	  A.	  Lourenço,	  R.	  Carreira,	  S.	  Carneiro,	  P.	  Maia,	  D.	  Glez-­‐Peña,	  F.	  Fdez-­‐Riverola,	  E.	  C.	  Ferreira,	  I.	  Rocha,	  M.	  Rocha,“@Note:	  a	  workbench	  for	  biomedical	  text	  mining.,”	  J.	  Biomed.	  Inform.,	  vol.	  42,	  no.	  4,	  pp.	  710–20,	  Aug.	  2009.	  

@Note2 open-source computational tools for biomedical text mining

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Universidade do Minho: RepositoriUM