Large-scale Information Extraction for Biomedical Literature · 2019-10-17 · Large-scale...
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. . Large-scale Information Extraction for Biomedical Literature 1st Swiss Text Analytics Conference (Swisstext 2016) Fabio Rinaldi, Lenz Furrer www.ontogene.org June 8, 2016 Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 1 / 34
Transcript of Large-scale Information Extraction for Biomedical Literature · 2019-10-17 · Large-scale...
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Large-scale Information Extractionfor Biomedical Literature
1st Swiss Text Analytics Conference (Swisstext 2016)
Fabio Rinaldi, Lenz Furrerwww.ontogene.org
June 8, 2016
Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 1 / 34
Outline
...1 Motivation
...2 Text mining in a curation workflow
...3 Large-scale detection of protein interactions
...4 Biomedical text mining: competitive evaluations
...5 The OntoGene approach and highlights
...6 Preview of recent work
Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 2 / 34
Flow of Information – Role of Curation
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Growth of Information
Pubmed citations 1986–2010
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Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 4 / 34
Outline
...1 Motivation
...2 Text mining in a curation workflow
...3 Large-scale detection of protein interactions
...4 Biomedical text mining: competitive evaluations
...5 The OntoGene approach and highlights
...6 Preview of recent work
Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 5 / 34
Assisted Curation
Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 6 / 34
RegulonDB
Escherichia coli K-12Transcriptional Regulatory Network.High-throughput literature curation of genetic regulation in bacterialmodels..
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Funded by the NIHGrant ID: GM110597 (NIGMS-NIH)Funding: $1.6 millionDuration: 4 years (Jan 2015 – Dec 2018)PI: Dr. Julio Collado-Vides (UNAM)Collaborators: Dr. Michael Savageau (UCDavis), Dr. Stephen Busby(Univ. of Birmingham), Dr. Fabio Rinaldi (Univ. Zurich)
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Assisted Curation: Example
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Topic: oxidative stress by OxyRCorpus: 46 papers, curated in RegDB
Methods: automated annotations of entities viaOntoGene, selection of sentences via ODINfilters, manual validation
Results: 100% of RIs retrieved, including TF,EFFECT and their TGIdentified the growth conditions for 15 of the20 RIs of OxyR checking only a limited set ofsentences (about 10% of the article is read)
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Assisted Curation: ODIN Interface
Lightweight browser-based graphical interfacePurpose: literature-based curation tasksCoupled with OntoGene pipelineEasily customizable
.Applications..
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Novartis (2008–2012)PharmGKB (2011)CTD (2012)RegulonDB (2013)
Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 9 / 34
Outline
...1 Motivation
...2 Text mining in a curation workflow
...3 Large-scale detection of protein interactions
...4 Biomedical text mining: competitive evaluations
...5 The OntoGene approach and highlights
...6 Preview of recent work
Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 10 / 34
Large scale mining of protein interactions
Text mining in an industrial contextConcept filtering and relation rankingCollection-based ranking
.Search interface built on Apache Solr..
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Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 11 / 34
Search Interface Apache Solr
Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 12 / 34
Search Interface Apache Solr
Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 13 / 34
Search Interface Apache Solr
Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 14 / 34
Outline
...1 Motivation
...2 Text mining in a curation workflow
...3 Large-scale detection of protein interactions
...4 Biomedical text mining: competitive evaluations
...5 The OntoGene approach and highlights
...6 Preview of recent work
Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 15 / 34
Competitive Evaluations
BioCreativeBioNLPCALBCCLEF-ERQA4MREDDI @ SemevalBioASQI2B2
Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 16 / 34
BioCreative Shared Task
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Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 17 / 34
BioCreative Shared Task
2004 (I) gene mentions, GO annotations2006 (II) GM, GN, PPI2009 (II.5) PPI2010 (III) GN, PPI-ACT, PPI-IMT, IAT2012 CTD-triage, curation workflow, IAT2013 (IV) BioC, CHEMDNER, CTD, GO, IAT2015 (V) BioC, CHEMDNER, Chem/Dis, BEL, IAT
Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 18 / 34
CTD-triage
.Purpose..
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“promotes understanding aboutthe effects of environmentalchemicals on human health byintegrating data from curatedscientific literature”.Task........entity extraction and triage Best overall results,
best detection of genes and diseases
Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 19 / 34
Outline
...1 Motivation
...2 Text mining in a curation workflow
...3 Large-scale detection of protein interactions
...4 Biomedical text mining: competitive evaluations
...5 The OntoGene approach and highlights
...6 Preview of recent work
Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 20 / 34
Single Term Resource
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Single Term Resource: Entities
Genes and proteins (NCBI gene, UniProt)Chemicals (MeSH, ChEBI, CTD)Diseases (MeSH, CTD)Organism and species (MeSH, NCBI taxonomy)Cell lines (Cellosaurus)
Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 22 / 34
Term Resource: Some Figures
genes/proteins chemicals diseases species cell lines total
count 10.4 M 979 k 67 k 1.3 M 36 k 12.8 Mavg. length 11.73 37.49 26.98 22.87 7.611 14.92terms/ID 1.1455 3.545 6.018 1.326 1.000 1.328IDs/term 1.371 1.049 1.000 1.003 1.004 1.306
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Term Resource: Annotated Document in ODIN
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Relation Extraction Pipeline
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Syntax-based Approach
“The neuronal nicotinic acetylcholine receptor alpha7 (nAChR alpha7) maybe involved in cognitive deficits in Schizophrenia and Alzheimer’s disease.”[PMID 15695160]
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Highlights
[2006] BioCreative II: PPI (3rd), IMT (best)[2009] BioCreative II.5 PPI (best results); BioNLP[2010] BioCreative III: ACT, IMT, IAT[2011] CALBC (large scale entity extraction), BioNLP[2012] CTD task at BioCreative 2012[2013] BioCreative IV: BioC, CTD, IAT80+ publications, 20+ journal articles
Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 27 / 34
Outline
...1 Motivation
...2 Text mining in a curation workflow
...3 Large-scale detection of protein interactions
...4 Biomedical text mining: competitive evaluations
...5 The OntoGene approach and highlights
...6 Preview of recent work
Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 28 / 34
Veterinary Pathology Text Mining
Collaboration with the veterinary facultyof the University of Bern.Task..
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Development and evaluation of an automated text-mining andsyndrome-classifying tool:
extract relevant information from pathology reports with minimalexpert interventionclassify pathology findings into syndromic groups to enhance theefficiency of health event detection
Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 29 / 34
Veterinary TM: Term Variation
volvulusdiagnosis
Volvulus
Voluvlus
Volvolus
VovulusDarmtorsion
Darmtorsionen
Dünndarmtorsion
Dünndarmdrehungen
Dünndarmvolvulus
Caecumtorsion
Zäkumtorsion
Zäkumstorsion
Caecum-Torsion
canonical term
inflectionmisspellings
synonym
synonyms
hyponym hyponym
spelling variants
Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 30 / 34
Veterinary TM: Enhancing Terminology
BelastungsmyopathieMuskeldegenerationMuskelfaserdegenerationMuskelfaserndegenerationMuskelzelldegenerationMuskelfasernekroseMuskelläsionenMuskelnekroseMuskelnekrosenMyodegenerationMyofaserdegenerationmyodegenerationMyonekrosemyonecrosisRhabdomyolyseWeiss-Muskel-KrankheitWeiss-MuskelkrankheitWeissmuskelerkrankungWeissmuskelkrankeitWeissmuskelkrankheit
C0234958
C0235957
C0035410
C0043153
Denegeration des Muskels
MUSKELDEGENERATION
Muskeldegeneration
MUSKELNEKROSE
Muskelnekrose
Myonekrose
RHABDOMYOLYSE
Rhabdomyolyse
Muskeldystrophie, nutritive
Weißmuskelkrankheit
Disease or Syndrome
Pathologic Function
UMLS datasyndrome: musculo-skeletaldiagnosis: myodegeneration
Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 31 / 34
PsyMine
.Text mining in support of psychiatric research:overcoming fragmented knowledge..
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Collaboration with the Compentence Center for Mental Health at theEpidemiology, Biostatistics and Prevention Institute
Goal: identify potential causes of mental diseasesMethods: analyse the whole biomedical literature, identify causes of
mental disorders (genetic/disease/social), rank and correlateVision: “global overview” of knowledge in literature
Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 32 / 34
MelanoBase
.Large-scale automatic extraction of actionable information from thebiomedical literature..
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integration with existing structured knowledgeuse-case scenario: melanomaresults to be integrated within the Melanoma Molecular Maprepository (S. Mocellin, Padua)collaborations with clinical researchers (Marisol Soengas, CNIO,Spain).
Fabio Rinaldi, Lenz Furrer www.ontogene.org Large-scale Biomedical Information Extraction 33 / 34
Summary
Text mining technologies can provide an effective support inbiomedical curationODIN is a user-friendly tool for text-mining supporting interactive(collaborative) curation of the biomedical literature.OntoGene provides competitive text mining technologies (BioCreative,CALBC prove quality)New projects and applications: VetSuisse, PsyMine, MelanoBase
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![Biomedical Information Extraction. Outline Intro to biomedical information extraction PASTA [Demetriou and Gaizauskas] Biomedical named entities Name.](https://static.fdocuments.net/doc/165x107/56649d4e5503460f94a2cf57/biomedical-information-extraction-outline-intro-to-biomedical-information.jpg)
