A software tool that classifies the mouse and human scientific literature in PubMed into different areas of research using citation networks and Medical Subject Heading MeSH Thesaurus to identify and study the popular areas of mouse-human research. It also classify the proteins in this literature citations into different biological systems using protein co-occurrence networks and Gene Ontology to investigate the proteins for which mouse is used as a model organism for human.

1. Mouse­Human
Research Classifier
Presented By: Osama Jomaa
Research Adviser: Dr. Iddo Friedberg

2. Mouse Models in Research

3. Mouse Models in Research
Shares 99% of its
genome with humans

4. Mouse Models in Research
Shares 99% of its
genome with humans
Fewer ethical
concerns than other
mammal models

5. Mouse Models in Research
InexpensiveShares 99% of its
genome with humans
Fewer ethical
concerns than other
mammal models
Short generation
times
Small

6. The Mouse Trap. The Danger of Using one Lab Animal to Study Every Disease. Daniel Engber
http:http://www.slate.com/articles/health_and_science/the_mouse_trap/2011/11/lab_mice_are_they_limiting_our_understanding_of_huma
n_disease_.html. November 16, 2011

7. Designer Mice for Human Research
Photo taken from “Designer mice for human disease - A close view of Nobel Laureate : Oliver Smithies” Yau-Sheng Tsai, Pei-Jane Tsai,
Man-Jin Jiang, Cherng-Shyang Chang. http://proj.ncku.edu.tw/research/commentary/e/20071116/2.html December 9, 2014

8. Mouse Model is Not Perfect Though
Photo taken from: The Mouse Trap. The Danger of Using one Lab Animal to Study Every Disease. Daniel Engber
http:http://www.slate.com/articles/health_and_science/the_mouse_trap/2011/11/lab_mice_are_they_limiting_our_understanding_of_huma
n_disease_.html. November 16, 2011

9. Mouse Correlation with Human to Equivalent Diseases
Photo taken from “Genomic responses in mouse models poorly mimic human inflammatory diseases.” Seok, Warren, and Others.
Proceedings of the National Academy of Sciences. 110, no. 9 (2013): 3507-3512.
Rank correlation (R2
)
Percentage of genes changed
in the same direction

10. Proposed Research
Classify the Mouse-Human scientific literature
in PubMed into different areas of research
Citation Networks + MeSH Thesaurus
Identify and study the popular areas of
Mouse-Human research
What?
How?
Why?

11. Proposed Research
Classify the proteins in the Mouse-Human
citation pairs into different biological systems
Protein Co-occurrence Networks
+ Gene Ontology
Investigate the biological systems and
proteins for which Mouse is used
as a model organism for Human
What?
How?
Why?

12. Agenda
1. PubMed Articles Classification
1. Collect Mouse and Human Papers
2. Build a Citation Network
3. Classify the Cit-Net Using MeSH Thesaurus
4. Stats Study on MeSH Disease Classification
2. PubMed Proteins Analysis
1. Collect Human Protein and Annotation Data
2. Build the Entity Co-occurrence Networks
3. Classify PCoC Networks Using Gene Ontology
3. Summary

13. 1. PubMed Articles Classification
1. Collect Mouse and Human Papers
2. Build a Citation Network
3. Classify the Cit-Net Using MeSH Thesaurus
4. Stats Study on MeSH Disease Classification
2. PubMed Proteins Analysis
1. Collect Human Proteins and Annotation Data
2. Build the Entity Co-occurrence Networks
3. Classify PCoC Networks Using Gene Ontology
3. Summary

14. Getting Mouse and Human PubMed IDs
Uniprot
GOA
Mouse PubMed Identifiers (PMIDs)
Human PubMed Identifiers (PMIDs)
1. Get Mouse & Human
papers from Uniprot

15. Getting Mouse and Human PubMed IDs
Uniprot
GOA
Mouse PubMed Identifiers (PMIDs)
Human PubMed Identifiers (PMIDs)
1. Get Mouse & Human
papers from Uniprot
2. Query PubMed API for the
citation list for each article

16. Getting Mouse and Human PubMed IDs
Uniprot
GOA
Mouse PubMed Identifiers (PMIDs)
Human PubMed Identifiers (PMIDs)
1. Get Mouse & Human
papers from Uniprot
2. Query PubMed API for the
citation list for each article
.
.
<CitationList>
<PMID> 342342 </PMID>
<PMID> 423545 </PMID>
<PMID> 432598 </PMID>
</CitationList>
.
.
3. Parse PubMed XML response
and get the citation list

17. Getting Mouse and Human PubMed IDs
Uniprot
GOA
Mouse PubMed Identifiers (PMIDs)
Human PubMed Identifiers (PMIDs)
1. Get Mouse & Human
papers from Uniprot
2. Query PubMed API for the
citation list for each article
.
.
<CitationList>
<PMID> 342342 </PMID>
<PMID> 423545 </PMID>
<PMID> 432598 </PMID>
</CitationList>
.
.
3. Parse PubMed XML response
and get the citation list
Very few PubMed articles have
the citation list in their XML file!

18. Getting Mouse and Human Citation
List from Scopus
Uniprot
GOA
Mouse PubMed Identifiers (PMIDs)
Human PubMed Identifiers (PMIDs)
1. Get Mouse & Human
papers from Uniprot
2. Author HTTP GET request
with PMIDS
3. Parse Scopus JSON response
and get the citation list
.
.
{CitationList: {PMID: 342342},
{PMID: 423545}, {PMID: 432598}}
.
.

19. 1. PubMed Articles Classification
1. Collect Mouse and Human Papers
2. Build a Citation Network
3. Classify the Cit-Net Using MeSH Thesaurus
4. Stats Study on MeSH Disease Classification
2. PubMed Proteins Analysis
1. Collect Human Proteins and Annotation Data
2. Build the Entity Co-occurrence Networks
3. Classify PCoC Networks Using Gene Ontology
3. Summary

20. Building the Citation Network
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21. Building the Citation Network
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M → H
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H → M
M → M

22. Building the Citation Network
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M → H
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62%
3%
34%
Mouse Inter and Intra Citations
Mouse-Human Citations Mouse-Mouse Citations
Moue-Others Citations
34%
62%
4%
Human Inter and Intra Citations
Human-Others Citations Human-Human Citations
Human-Mouse Citations

23. 1. PubMed Articles Classification
1. Collect Mouse and Human Papers
2. Build a Citation Network
3. Classify the Cit-Net Using MeSH Thesaurus
4. Stats Study on MeSH Disease Classification
2. PubMed Proteins Analysis
1. Collect Human Proteins and Annotation Data
2. Build the Entity Co-occurrence Networks
3. Classify PCoC Networks Using Gene Ontology
3. Summary

24. Medical Subject Headings
 Controlled vocabulary to index PubMed articles
 Stored in a DAG-like structure
 16 top level concepts at the root
 Includes ~27K concepts (MeSH descriptors) all together

25. Medical Subject Headings
 Controlled vocabulary to index PubMed articles
 Stored in a DAG-like structure
 16 top level concepts at the root
 Includes ~27K concepts (MeSH descriptors) all together
We used MeSH to group the Mouse and
Human papers in the citation network
into classes of research

26. MeSH Structure Example
Digestive System Diseases
Gastrointestinal Diseases
Digestive System Neoplasms
Neoplasms by Site
Neoplasms
Stomach Diseases
Gastrointestinal Neoplasms
Stomach Neoplasms

27. Classifying the Citation Network
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28. To Do: Place in research areas
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H Digestive
System
Diseases
Eye Diseases
Virus
Diseases
Immune
System
Diseases
Cardiovascular DiseasesSkin
Diseases

29. 1. PubMed Articles Classification
1. Collect Mouse and Human Papers
2. Build a Citation Network
3. Classify the Cit-Net Using MeSH Thesaurus
4. Stats Study on MeSH Disease Classification
2. PubMed Proteins Analysis
1. Collect Human Proteins and Annotation Data
2. Build the Entity Co-occurrence Networks
3. Classify PCoC Networks Using Gene Ontology
3. Summary

30. Number of Mouse and Human Papers in the MeSH
Disease Categories

31. Number of Mouse-Human Citation Pairs in the MeSH
Disease Categories

32. 1. PubMed Articles Classification
1. Collect Mouse and Human Papers
2. Build a Citation Network
3. Classify the Cit-Net Using MeSH Thesaurus
4. Stats Study on MeSH Disease Classification
2. PubMed Proteins Analysis
1. Collect Human Proteins and Annotation Data
2. Build the Entity Co-occurrence Networks
3. Classify PCoC Networks Using Gene Ontology
3. Summary

33. GenBank
Protein: NP_e342 | PMID: 432432
kicgdkssgihygvitcegckgffrrsqqc
Protein: NP_452u1 | PMID: 483232
Adtltytlglsdgqlplgaspdlpeasacp
…..
1. Get the protein sequences Human
and papers

34. GenBank
Protein: NP_e342 | PMID: 432432
kicgdkssgihygvitcegckgffrrsqqc
Protein: NP_452u1 | PMID: 483232
Adtltytlglsdgqlplgaspdlpeasacp
…..
1. Get the protein sequences Human
and papers
...
PMID: 3213414
NP_u4323: sgihygvitcegckgffrrsqqc
NP_i4322: lplgaspdlpeasacfewrwts
NP_w3421: kicgdkssgihygvitceg
PMID: 2346414
NP_ti3423: vitcegckgckgffrrsqqc
NP_q4322f: ygvitcegeasacfewrwts
NP_x342u2: kicgdkssgihygvitceg
2. Group the proteins by their PMID

35. GenBank
Protein: NP_e342 | PMID: 432432
kicgdkssgihygvitcegckgffrrsqqc
Protein: NP_452u1 | PMID: 483232
Adtltytlglsdgqlplgaspdlpeasacp
…..
1. Get the protein sequences Human
and papers
...
PMID: 3213414
NP_u4323: sgihygvitcegckgffrrsqqc
NP_i4322: lplgaspdlpeasacfewrwts
NP_w3421: kicgdkssgihygvitceg
PMID: 2346414
NP_ti3423: vitcegckgckgffrrsqqc
NP_q4322f: ygvitcegeasacfewrwts
NP_x342u2: kicgdkssgihygvitceg
NP_u4323: sgihygvitcegckgffrrsqqc
NP_i4322: lplgaspdlpeasacfewrwts
NP_w3421: kicgdkssgihygvitceg
NP_ti3423: vitcegckgckgffrrsqqc
NP_q4322f: ygvitcegeasacfewrwts
NP_x342u2: kicgdkssgihygvitceg
NP_w3421: kicgdkssgihygvitceg
NP_ti3423: vitcegckgckgffrrsqqc
2. Group the proteins by their PMID
3. Intersect the Genbank papers with Scopus citations

36. NP_u4323: sgihygvitcegckgffrrsqqc
NP_i4322: lplgaspdlpeasacfewrwts
NP_w3421: kicgdkssgihygvitceg
NP_ti3423: vitcegckgckgffrrsqqc
NP_q4322f: ygvitcegeasacfewrwts
NP_x342u2: kicgdkssgihygvitceg
NP_w3421: kicgdkssgihygvitceg
NP_ti3423: vitcegckgckgffrrsqqc
NP_u4323: sgihygvitcegckgffrrsqqc
NP_i4322: lplgaspdlpeasacfewrwts
NP_w3421: kicgdkssgihygvitceg
NP_ti3423: vitcegckgckgffrrsqqc
NP_q4322f: ygvitcegeasacfewrwts
NP_x342u2: kicgdkssgihygvitceg
Removing Redundancies
Use CD-HIT with similarity threshold = 0.9

37. Gene Ontology
Photo taken from: Gene Ontology Consortium. Ontology Structure. http://geneontology.org/page/ontology-structure Last access
December 13, 2014

38. Gene Ontology Annotation
Biological Process
Cellular Component
Molecular Function
cytochrome c
mitochondrial matrix
oxidoreductase activity
oxidative phosphorylation

39. NP_u4323: sgihygvitcegckgffrrsqqc
NP_i4322: lplgaspdlpeasacfewrwts
NP_w3421: kicgdkssgihygvitceg
NP_ti3423: vitcegckgckgffrrsqqc
NP_q4322f: ygvitcegeasacfewrwts
NP_x342u2: kicgdkssgihygvitceg
FASTA File
BLAST
DB
1. Create BLAST query in FASTA format
2. Create BLAST Database from Swissprot
Human Flat File
Getting GO Terms

40. NP_u4323: sgihygvitcegckgffrrsqqc
NP_i4322: lplgaspdlpeasacfewrwts
NP_w3421: kicgdkssgihygvitceg
NP_ti3423: vitcegckgckgffrrsqqc
NP_q4322f: ygvitcegeasacfewrwts
NP_x342u2: kicgdkssgihygvitceg
FASTA File
BLAST
DB
NP_u4323: GO1, GO5, GO4
NP_i4322: GO5, GO9
NP_w3421: GO4, GO6
...
1. Create BLAST query in FASTA format
2. Create BLAST Database from Swissprot
Human Flat File
3. Do BLAST with e-value = 10-8
4. Parse the BLAST XML response
and get the GO terms for the top hits
Getting GO Terms

41. 1. PubMed Articles Classification
1. Collect Mouse and Human Papers
2. Build a Citation Network
3. Classify the Cit-Net Using MeSH Thesaurus
4. Stats Study on MeSH Disease Classification
2. PubMed Proteins Analysis
1. Collect Cited Human Proteins and Annotation Data
2. Build the Entity Co-occurrence Networks
3. Classify PCoC Networks Using Gene Ontology
3. Summary

42. MP
MP
MP
HP
HP

43. 1
12
6
MP
MP
MP
HP
HP
10
5

44. 20
14
1
19
12
18
24
7
8
4
6
MP
MP
MP
HP
HP
Citation Edge
P-P Edge
P-C-P Edge

45. Building the PCoC Network

46. 1. PubMed Articles Classification
1. Collect Mouse and Human Papers
2. Build a Citation Network
3. Classify the Cit-Net Using MeSH Thesaurus
4. Stats Study on MeSH Disease Classification
2. PubMed Proteins Analysis
1. Collect Human Proteins and Annotation Data
2. Build the Entity Co-occurrence Networks
3. Classify PCoC Networks Using Gene Ontology
3. Summary

47. To Do: Classifying the PCoC Network

48. To Do: Place in Protein Biological Systems
lactase activity
serotonin
Receptor
activity
signal sequence
binding
signal transducer
activitynucleotide
binding
ATP
binding

49. 1. PubMed Articles Classification
1. Collect Mouse and Human Papers
2. Build a Citation Network
3. Classify the Cit-Net Using MeSH Thesaurus
4. Stats Study on MeSH Disease Classification
2. PubMed Proteins Analysis
1. Collect Human Proteins and Annotation Data
2. Build the Entity Co-occurrence Networks
3. Classify PCoC Networks Using Gene Ontology
3. Summary

50. Summary
 Cit-Net connects citing Mouse papers with cited Human
papers in the PubMed database
 MeSH is used to classify the citation network nodes into
different classes of research
 PCoC network connects the proteins in the citing Mouse
papers with proteins in the cited Human papers
 GO is used to group the P-P and P-C-P network nodes
into different classes of MFs, BPs and Ccs

51. Timetable
Jan Feb Mar Apr May
Database Creation and
Data migration
Citation Network
Classification
PCoC Networks Building
PCoC Networks
Classification
PCoC Networks Analysis

52. Thank You!
Q & A