Medical network analysis: Linking diseases and genes through data and text mining

Lars Juhl Jensen

Medical network analysisLinking diseases and genes through

data and text mining

electronic health registries

disease trajectories

community resources

linking genes and diseases

electronic health registries

Jensen et al., Nature Reviews Genetics, 2012

unstructured data

structured data

Jensen et al., Nature Reviews Genetics, 2012

civil registration system

established in 1968

CPR number

Jensen et al., Nature Reviews Genetics, 2012

national discharge registry

14 years

6.2 million patients

119 million diagnoses

Jensen et al., Nature Reviews Genetics, 2012

reimbursement

statistical analysis

comorbidity

contingency table

Jensen et al., Nature Reviews Genetics, 2012

confounding factors

“known knowns”

sex

age

type of hospital encounter

Jensen et al., Nature Communications, 2014

“known unknowns”

smoking

diet

“unknown unknowns”

reporting biases

matched controls

temporal correlations

disease trajectories

Jensen et al., Nature Communications, 2014

clustering

trajectory networks

Jensen et al., Nature Communications, 2014

specific questions

alcohol-related sepsis

Beck et al., Scientific Reports, 2016

community resources

string-db.org

functional associations

DISEASES

disease–gene associations

curated knowledge

protein complexes

pathways

established disease genes

experimental data

physical interactions

Jensen & Bork, Science, 2008

coexpression

GWAS

text mining

>10 km

named entity recognition

gene/protein dictionary

disease dictionary

many databases

different formats

different identifiers

variable quality

not comparable

hard work

(Ph.D. students)

quality scores

affinity purification

von Mering et al., Nucleic Acids Research, 2005

cooccurrence score

score calibration

von Mering et al., Nucleic Acids Research, 2005

implicit weighting by quality

common scale

visualization

Cytoscape

Thank you