This document discusses text and data mining for biomedical research. It describes how literature and molecular data have grown exponentially and challenges of accessing full texts. It outlines applications of text mining including document retrieval, discovery of gene-disease associations, and surveillance of surgical infections. Challenges include accuracy, moving from abstracts to full texts, and file formats.

1. Text and data mining for
Biomedical Research
Dr. Jean-Fred Fontaine
Max Delbrück Center for Molecular Medicine, Berlin

2. Scientific project and biomedical literature
Project design
Project design
• State of the art
• Innovative ideas
Communication
Communication
Experiments
Experiments
• Technologies
• State of the art
• Explanations
• Open hypotheses
• Perspectives
Analysis
Analysis
• Methods
• Explanations
• New hypotheses

3. Data growth
Literature growth
Molecular data growth

4. Accessibility
18 M (all)
9.7 M – TEXT MINING OF ABSTRACTS
8.6 M
2.4 M – (freely readable)
1.8 M
0.2 M - TEXT MINING OF FULL
TEXTS*
Krallinger et al. (2010) Methods Mol Biol.
* PMC Open Access subset (2012): 249,108 full texts (Ortuno et al., 2013)

5. Document retrieval
Alzheimer’s disease?
Citations in PubMed®
25,000,000
20,000,000
15,000,000
10,000,000
0
4
9
1
8
2
5
9
1
6
0
9
1
4
6
8
9
1
2
7
6
9
1
0
8
4
9
1
8
2
9
1
6
0
2
4
8
0
2
5,000,000
0
By date
Medline Ranker
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By relevance
Fontaine et al. (2009) Nucleic Acids Res.
http://cbdm.mdc-berlin.de/tools/medlineranker/

6. Discovery of gene-disease associations
Database mining
Database mining
Medline Ranker / Génie
...
...
Rank 20 000 genes
Fontaine et al. (2011) Nucleic Acids Res.
http://cbdm.mdc-berlin.de/tools/genie

7. Discovery of gene- and drug-disease associations
?
Before 2007
Before 2007
After 2007
After 2007
Frijters et al. (2010) PLoS Comput Biol.

8. Semantic analysis

Knowledge bases
Van Landeghem et al. (2013) PLoS One.

9. Network construction
Modelling Plant Defence Response
Miljkovic et al. (2012) PLoS One.

10. Trends
Palidwor & Andrade-Navarro (2010) J Biomed Discov Collab.
http:// www.ogic.ca/mltrends/

11. Surveillance of Surgical Site Infections
 University Hospital of Rennes, France
 SSI secondary to neurosurgery
 Electronic Patient Records
 ICD10 codes
 Free text
2008-2009
2008-2009
relevant
relevant
records
records
Conventional ICD10 codes
surveillance
Full-text
medical
reports
TRUE positive
Classification
Classification
11
12
FALSE positive
0
219
18
FALSE negative
10
2
1
TRUE negative
2010 medical reports
3
1212
993
1194
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.......
.......
Campillo-Gimenez et al. (2013) Stud Health Technol Inform.

12. Disease Correlations from Electronic Patient Records

ICD10 codes
ICD10 codes
Avg. ICD10 codes

Manual: 2.7

Text Mining: 9.5
Manual
Patient records
Patient records
Text Mining

Co-morbidity

93 / 802 unexpected

Ex. Alopecia and Migraine
Alopecia
HR
THRA
ESR1
Migraine
Roque et al. (2011) PLoS Comput Biol.

13. Summary

Computers and biomedical literature and data




Generation
Storage
Analysis
Text and data mining


Useful from project start to finish
Broad and critical applications


Information extraction

Knowledge databases


Information retrieval
Knowledge discovery
Limited by text availability

14. Challenges

Accuracy in some applications

Ambiguity, complex sentences, document context, novelty


From abstracts to full texts




“Protein A and its partners”
Current methods optimized for short texts (abstracts)
Figures and tables
Supplementary information
File format

The PDF problem
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XML: structured format

Abstract, Introduction, Results, Methods, Discussion, References, ...

15. Needs

Copyright



Teach scientists
Unify licenses
Availability

All significant documents


Articles, reviews, case reports, letters
The main structured text (XML)

No figures (or optional)



Supplements: optional
No fancy user interface or webservice


texts mostly useless for readers
FTP/P2P + Compressed XML
Communicating Research results



# articles
Compressed file size*
1
13 KB
1M
12 GB
20M
250 GB
Open Access
As text
As data


standardized list of facts
standardized figures data and tables
* Projections based on PMC Open Access 2012