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The pragmatic text miner: It’s just another type of poorly standardized data

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Lars Juhl Jensen
Lars Juhl Jensen

This document discusses text mining and challenges working with unstructured biomedical data from various sources. It describes using named entity recognition, expansion rules, and co-mentioning within texts to identify related entities. Integrating text mining with curated knowledge bases and other data can provide more insights but requires common identifiers and dealing with different formats, licenses, and qualities of information from multiple sources. Collaboration is important to make the most of technology and domain expertise.

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Lars Juhl Jensen The pragmatic text miner It’s just another type of poorly standardized data
why text mining?
data mining
guilt by association
structured data
unstructured text
biomedical literature
>10 km
too much to read
computer
as smart as a dog
teach it specific tricks
named entity recognition
comprehensive lexicon
synonyms
prostate specific antigen
KLK3
expansion rules
prefixes and suffixes
KLK3
hKLK3
flexible matching
hyphens and spaces
prostate specific antigen
prostate-specific antigen
“black list”
SDS
co-mentioning
within documents
within paragraphs
within sentences
weighted score
text corpus
what we normally use
Medline abstracts
what we should use
full-text articles
many publishers
different interfaces
different formats
different licenses
unclear terms
unifying text & data
text mining
curated knowledge
experimental data
computational predictions
integrated web resources
protein networks
string-db.org
chemical networks
stitch-db.org
subcellular localization
compartments.jensenlab.org
tissue expression
tissues.jensenlab.org
disease associations
many sources
different formats
different identifiers
variable quality
not comparable
hard work
common identifiers
quality scores
score calibration
data visualization
collaboration model
domain experts
what?
why?
problem
manpower
me
how?
technology
guidance
biodiversity
organisms
environments
Encyclopedia of Life
Biodiversity Heritage Library
pharmacovigilance
drugs
adverse drug reactions
electronic health records
what we need
one place to get all
the format is not crucial
the license is
Acknowledgments Protein networks Michael Kuhn Damian Szklarczyk Andrea Franceschini Milan Simonovic Alexander Roth Sune Pletscher- Frankild Jianyi Lin Pablo Minguez Christian von Mering Peer Bork Localization and disease Sune Pletscher-Frankild Alberto Santos Janos Binder Kalliopi Tsafou Christian Stolte Albert Palleja Heiko Horn Evangelos Pafilis Reinhardt Schneider Sean O’ Donoghue

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