NLP Tales in Biomedicine (introductory presentation for the Auckland NLP MeetUp group by Anna Divoli)
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NLP Tales in Biomedicine (introductory presentation for the Auckland NLP MeetUp group by Anna Divoli)

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Slides from talk:
NLP tales in Biomedicine
Auckland MeetUp group, June 2014
http://www.meetup.com/Natural-Language-Processing-in-NZ/events/184030662/

Mining text to answer biomedical questions is a fascinating applied research area. The biomedical domain is one of the first 'big data' domains. It attracts people from the domain itself passionate to answer pressing scientific questions as well as computer scientists and linguists who see a domain with great standards, resources and numerous applications.

During this talk I will give you a brief overview of different NLP problems in the biomedical domain and I'll make comparisons to mainstream NLP applications (e.g., search) and other, more commercial domains (e.g., voice of customer). My aim is to introduce you to a domain with state of the art solutions, free high-quality resources and well developed methodologies. If I inspire anyone to work on challenging biomedical problems, will be a bonus!

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NLP Tales in Biomedicine (introductory presentation for the Auckland NLP MeetUp group by Anna Divoli) NLP Tales in Biomedicine (introductory presentation for the Auckland NLP MeetUp group by Anna Divoli) Presentation Transcript

  • NLP Tales in Biomedicine Anna Divoli @annadivoli nelshami.deviantart.com Auckland NLP MeetUp June 2014
  • Biology recap Traits Discussions DNA > Genes > Proteins > Phenotype: Function > Emotions Disease Medical Notes (scientific literature)
  • Information of interest Genes / Proteins  specific information for database annotation Gene names: tinman, lilliputian, dreadlocks, lush, cheap date, methuselah, Van Gogh, maggie, brainiac, grim, reaper, cleopatra, swiss cheese, ken and barbie, kenny, out cold, lava lamp, hamlet, sonic hedgehog, werewolf, half pint, fucK, drop dead, chardonnay, agnostic, I’m not dead yet…
  • imp.princeton.edu/static//css/images/network.png Information of interest Genes / Proteins  relationships & network building www.frontiersin.org/files/Articles/77923
  • Proteins: their sub-cellular location, their structure, the conditions of their expression, their interactions, disease associations… Disease – Drug: interactions, adverse effects, secondary indications… Other entities: organs/tissues, metabolites/chemicals, phenotypes… Detecting: methodologies & findings in experimental papers, paradigm shifts… Systems for specific: diseases, pathways, drug targets, organisms… Examples of information of interest
  • Don Swanson’s ABC model: dietary fish oil reduction of: blood viscosity, platelet aggregability, vascular reactivity Raynaud’s disease - Swanson, D. R. (1986). Fish oil, Raynaud's syndrome and undiscovered public knowledge. Perspectives in Biology and Medicine, 30(1): 7-18. - Swanson, D. R. (1987). Two medical literatures that are logically but not bibliographically connected. Journal of the American Society for Information Science 38: 228-233. Literature-based Discovery: Text mining! causes ameliorates
  • Don Swanson’s ABC model: dietary fish oil reduction of: blood viscosity, platelet aggregability, vascular reactivity Raynaud’s disease - Swanson, D. R. (1986). Fish oil, Raynaud's syndrome and undiscovered public knowledge. Perspectives in Biology and Medicine, 30(1): 7-18. - Swanson, D. R. (1987). Two medical literatures that are logically but not bibliographically connected. Journal of the American Society for Information Science 38: 228-233. Literature-based Discovery: Text mining! causes ameliorates
  • Ontologies OBO Foundry: GO CHEBI… Medical: UMLS SNOMED CT IDC… MeSH
  • Search
  • Search Search query: SAF LTR Looking for: interactions between SAF and viral LTR elements (SAF is a transcription factor, LTR stands for ‘long terminal repeat’) but also: SAF: Single And Free LTR: Long Term Relationship  better to use domain specific resources in occassions like this 
  • Search
  • Search
  • Search
  • Search
  • Search
  • Search
  • Search
  • Search
  • Search
  • Social Biomedicine News Patient Forums Blogs Twitter …
  • Social Biomedicine News Patient Forums Blogs Twitter …
  • Social Biomedicine News Patient Forums Blogs Twitter …
  • Social Biomedicine News Patient Forums Blogs Twitter … 344 online forum posts on Depression Source: www.patient.co.uk Date: July 2013
  • Social Biomedicine News Patient Forums Blogs Twitter … 344 online forum posts on Depression Source: www.patient.co.uk Date: July 2013
  • Social Biomedicine News Patient Forums Blogs Twitter … 344 online forum posts on Depression Source: www.patient.co.uk Date: July 2013
  • Social Biomedicine News Patient Forums Blogs Twitter … 344 online forum posts on Depression Source: www.patient.co.uk Date: July 2013
  • Citation Analysis From: clinical meta-analysis… to: detect information for knowledge augmentation and summarization…
  • Citation Analysis From: clinical meta-analysis… to: detect information for knowledge augmentation and summarization…
  • Citation Analysis From: clinical meta-analysis… to: detect information for knowledge augmentation and summarization…
  • Growing field: publications over the past 20 years “text mining” ontology
  • Summary Entities – Relationships/Interactions Resources: Databases, Ontologies, Corpora… Networks: Systems Biology, Translational Medicine, Literature-based Discovery End Users – Search Social Biomedicine Citation analysis … and this is just a 30 min introduction…
  • Resources Databases (from genes to literature): http://www.ncbi.nlm.nih.gov/ http://www.ebi.ac.uk/services Ontologies & Linked Data: http://www.obofoundry.org/ http://www.nlm.nih.gov/research/umls/ http://linkedlifedata.com/ Corpora: http://compbio.ucdenver.edu/ccp/corpora/obtaining.shtml http://www.nactem.ac.uk/resources.php