Large-scale integration of data and text

Lars Juhl Jensen

Large-scale integration of data and text

Lars Juhl Jensen

association networks

text mining

localization and diseases

me

promoter analysis

Jensen & Knudsen, Bioinformatics, 2000

function prediction

Jensen, Gupta et al., Journal of Molecular Biology, 2002

protein networks

de Lichtenberg, Jensen et al., Science, 2005

chemoinformatics

Campillos, Kuhn et al., Science, 2008

data mining

text mining

electronic health records

association networks

guilt by association

STRING

~2.6 million proteins

Szklarczyk, Franceschini et al., Nucleic Acids Research, 2011

STITCH

~300,000 small molecules

Kuhn et al., Nucleic Acids Research, 2012

genomic context

gene fusion

Korbel et al., Nature Biotechnology, 2004

operons

Korbel et al., Nature Biotechnology, 2004

bidirectional promoters

Korbel et al., Nature Biotechnology, 2004

metagenome neighborhood

Harrington et al., PNAS, 2007

phylogenetic profiles

Korbel et al., Nature Biotechnology, 2004

a real example

Cell

Cellulosomes

Cellulose

experimental data

gene coexpression

protein interactions

Jensen & Bork, Science, 2008

curated knowledge

drug targets

complexes

pathways

Letunic & Bork, Trends in Biochemical Sciences, 2008

many databases

different formats

different identifiers

variable quality

not comparable

hard work

quality scores

von Mering et al., Nucleic Acids Research, 2005

calibrate vs. gold standard

missing most of the data

text mining

>10 km

too much to read

computer

as smart as a dog

teach it specific tricks

named entity recognition

comprehensive lexicon

cyclin dependent kinase 1

CDK1

CDC2

flexible matching

spaces and hyphens

cyclin dependent kinase 1

cyclin-dependent kinase 1

orthographic variation

CDC2

hCdc2

“black list”

SDS

information extraction

count co-mentioning

within documents

within paragraphs

within sentences

scoring scheme

corpora

~22 million abstracts

no access

~4 million full-text articles

augmented browsing

Reflect

browser add-on

real-time text mining

Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009O’Donoghue et al., Journal of Web Semantics, 2010

localization and disease

small molecules

proteins

compartments

tissues

diseases

organisms

environments

suite of web resources

common backend database

jensenlab.org

text mining

curated knowledge

experimental data

computational predictions

quality scores

web-centric databases

DISEASES

visualization

COMPARTMENTS

compartments.jensenlab.org

TISSUES

tissues.jensenlab.org

project onto networks

Szklarczyk, Franceschini et al., Nucleic Acids Research, 2011

compartments.jensenlab.org

tissues.jensenlab.org

diseases.jensenlab.org

summary

bioinformatics

more than alignment

data/text mining

save you much time

Acknowledgments

STRING/STITCHChristian von Mering

Damian Szklarczyk

Michael Kuhn

Manuel Stark

Samuel Chaffron

Chris Creevey

Jean Muller

Tobias Doerks

Philippe Julien

Alexander Roth

Milan Simonovic

Jan Korbel

Berend Snel

Martijn Huynen

Peer Bork

Literature miningSune Frankild

Evangelos Pafilis

Janos Binder

Kalliopi Tsafou

Alberto Santos

Heiko Horn

Michael Kuhn

Nigel Brown

Reinhardt Schneider

Sean O’Donoghue

Questions?