1. Finding Contradictions, Contrasts and
Negations in Protein­Protein Interactions
in the Biomedical Literature
Using Text Mining
Farzaneh Sarafraz
COMP7000 Presentation
Text Mining Group
Supervised by Dr. Goran Nenadić
28 November 2008

2. Natural Language Processing
a.k.a Computational Linguistics

3. Natural Language Processing

4. Natural Language Processing

5. Text
Easily shared

Primary information source

Most up­to­date

Unstructured

No explicit data


6. Text Mining
IE
IR DM

7. Finding Contradictions, Contrasts and
Negations in Protein­Protein
Interactions
in the Biomedical Literature
Using Text Mining

8. Text Mining in Biomedicine
Biomedical Scientific Literature

>17M articles from >5K journals

since 1950s adding 2000 every day
Impossible for humans to manage

Specific (rather peculiar) language


9. Finding Contradictions, Contrasts and
Negations in Protein­Protein
Interactions
in the Biomedical Literature
Using Text Mining

10. Protein­Protein Interactions

11. Protein­Protein Interactions

12. Finding Contradictions, Contrasts and
Negations in Protein­Protein
Interactions
in the Biomedical Literature
Using Text Mining

13. Example
quot;Our results indicate that gp120 from two

different strains of HIV binds to a larger
region of the CD4 protein than previously
described.quot;

14. Example
gp120 synonyms

− gp41
− Transmembrane Glycoprotein − TM
− Envelope Glycoprotein − ENV
− Surface Glycoprotein − HIV­1 gp8
− SU
− gp160
− Envelope Surface Glycoprotein gp160 Precursor

15. Example
Binds is almost the same as

− Interacts with
− Frictionates
− Associates with
− Activates
− Colocalizes with
− Cleaves

16. Example
CD4 can be expressed as

CD4+ T T4(CD) CD4+ (T) CD4(+) T cell
CD4­, T CD4 (T) CD4(T) CD4 T­cell
T CD4 CD4(+)T CD(4+) T CD4(+) T­cell
CD4(+) T CD4+­T CD4­ T CD4(+)T cell
CD4 T CD4(+)­T CD4+ T cell CD4+­T­cell
T4+ (CD) CD4+T CD4­, T cell CD4(+)­T­cell
T4 (CD) T (CD4) CD4+ T­cell CD4 T cell

17. Even after all this...
The chimpanzee­based CD4(81­92) peptide,

however, which differs from the human peptide
by a single amino acid substitution (E for G) at
position 87, was considerably less potent than
the human CD4(81­92)­based peptide congener
to inhibit HIV­1­induced cell­cell fusion.

18. Contradiction and Contrasts
Author A reports p

Author B reports ¬p

We have p under conditions q

But we have ¬p under conditions q'


19. Negations
Linguistic

− quot;Protein A does not interact with protein B.quot;
− quot;We lack evidence that A interacts with B.quot;
Biological

− quot;Protein A inhibits protein B.quot;
− dephosphorylates / depolymerizes
− downregulates (vs. upregulates)
− etc.

20. Finding Contradictions, Contrasts and
Negations in Protein­Protein
Interactions
in the Biomedical Literature
Using Text Mining

21. Framework
HIV­1 and Human Protein­Protein interactions

− Manually over 7 years
− >3000 journal papers
− >5000 tuples
− Gold standard
Other negative reports

− Journal of Negative Results in BioMedicine
Other gold standards


22. Detecting Protein­Protein Interactions
Recognize gene/protein names

− State of the art ~ 87%
Identify gene/protein names

Detect the interaction and its qualities

− 70 quot;differentquot; interactions in reference DB

23. Protein Name Identification
1500 human proteins

− State of the art ~ 87%
− Available tools ~ 15%
− Our method ~ 35%
20 HIV proteins

− No available tool
− Our method ~ 95%

24. Applications
Contradictions and Negations
 
Contrast
Other diseases New HIV­1 literature
 

25. Achieved so far & plan for future
Reproduce the HIV­1 interactions database

Designed an interaction ontology

Identify patterns of negation, contradiction,

contrast
Use the above data to increase the annotation

accuracy

26. Evaluation
Widely used evalutation measures

− Precision, Recall, F­Score
− Sensitivity and Specificity
Benchmarks and datasets used in challenges

Manually annotated gold standards


27. Summary
Finding Contradictions, Contrasts and
Negations in Protein­Protein
Interactions
in the Biomedical Literature
Using Text Mining