Talk given at the UNM BioMedical Informatics Seminar Series, Oct 15, 2015. Because the languages of computing are numerous and diverse, it can be challenging to choose an appropriate language for a given task. Yet data are of little value unless represented by semantic systems of languages with appropriate levels of abstraction. We consider the analogy between object-oriented programming and abstraction in biomedical vocabulary and the Sapir-Whorf Hypothesis (that an individual’s thoughts and actions are determined by the language he or she speaks). As an example, we consider the differences between ICD-10 and disease ontology.

1. The Language Diversity
of Computing
Or, how to talk with a computer.
Jeremy Yang
(Mgr., Systems & Programming)
Translational Informatics Div.
Dept. of Internal Medicine
University of New Mexico
BioMISS -- Thursday, Oct 15, 2015 1

2. Language Diversity Examples
Python Perl Fortran C R
C++ Java Basic SQL Sparql
XML XSD XPath URLs bash
HTML HTTP ASCII UTF-8 regex
Scala ICD-10 Ruby OWL RDF
2

3. A Working Definition of “Language”
● Coherent symbology (symbolic system)
3

4. Languages: Some major advances
COBOL
(1960) Sparql
(2008)
Java
(1995)
4
1950
FORTRAN
(1953)
1960 1970 1980 1990 2000 2010
SQL
(1979)
C
(1969)
C++
(1979)
Perl
(1987)
Python
(1989)
HTML
(1990)
XML
(1997)
RDF
(1999)

5. Language merit vs. elitism
5

6. Why do we care about languages?
● Compatibility
● Efficiency
● Usability
● Knowledge
representation
● Intelligence
● Evolution
Naturellement!
6

7. 7
℅ Prof Harald Sack,
Hasso Plattner
Institute, U.
Potsdam, MOOC:
“Semantic Web
Technologies”

8. Programming paradigms
Object Oriented
● classes
● instances
● methods
● ~ nouns
8
Functional
● functions
● routines
● parameters
● ~ verbs
Programming paradigms are language paradigms.

9. 9
Object Oriented
Example:
CDK = Chemistry
Development Kit
Open source Java
package & API
Computers have
“evolved” from
numerical calculators to
knowledge processors.
Knowledge
representation and
processing via
language!

10. 10
Italian Music Terms
Choice of language should be guided by the domain.

11. Q: So what is the problem?
A: Language gaps
CODE
JARGON
MEANING
“Interpretation”
MATH
11

12. Q: So what is the problem?
A: Standards (so many!)
“Why can’t my iPhone talk to my ...”
● TV
● Audio system
● Car
● Medical records
12

13. Q: So what is the problem?
A: Language shapes, empowers,
limits thought. (Sapir-Whorf
Hypothesis, aka Linguistic
Relativity)
13

14. Q: So what is the problem?
A: Abstraction
● Overgeneralizing
● Reality is concrete!
● But: abstraction organizes knowledge
● (a feature, not a bug!)
14
“We think in generalities, but we live in detail.” -- Alfred North Whitehead

15. 15
Abstraction:
Shakespeare quotes
“Full of sound and fury, signifying nothing.”

16. 16
"On to this one quicker than
a jackrabbit on a hot date.
Look at this finish! That is
beyond world class."
"Braver than a matador in a
pink tutu he was."
"Racing Santander’s butcher
men tried to hack down Xavi.
Xavi dancing over the
combine harvesters that are
coming after him."
“He could make an onion
cry.” (on Lionel Messi) "Where the insane
becomes the routine with
this man. He is nothing less
than a ball whisperer."
Abstraction:
Ray Hudson Quotes

17. 17
“You campaign in poetry. You
govern in prose.” - Mario Cuomo
But maybe all language is poetic.

18. Languages of Biomedical Knowledge
18

19. 19
Which cirrhosis?
Specificity?
http://apps.who.int/classifications/icd10

20. Translation
and
mapping
terms
20
story
history

21. Our project:
Illuminating the Druggable Genome (IDG)
$4.9M
21

22. Illuminating the Druggable Genome
Knowledge Management Center (IDG-KMC)
Translational Informatics Division
Chief: Tudor Oprea, MD, PhD
IDG-KMC Workflow
22

23. IDG-KMC Collaborator Network
23

24. Slide ℅ Tudor Oprea
24
Heterogeneous data integration. Language diversity.

25. IDG-KMC
Language
Challenge:
Case #1:
Drug
Nomenclature
25http://pasilla.health.unm.edu/tomcat/drugdb

26. IDG-KMC
Language
Challenge:
Case #2:
Disease
Nomenclature
26

27. 27
ICD Disease Ontology
● The International Classification of
Diseases (ICD) is the standard
diagnostic tool for epidemiology,
health management and clinical
purposes.
● WHO
● Clinical emphasis
● Procedures (CM)
● EMR
● Versions
● The mission the Disease Ontology
(DO) is to provide an open source
ontology for the integration of
biomedical data that is associated
with human disease.
● Academic network
● Research emphasis
● Community driven
● Continual updates

28. Disease nomenclature
● Nosology, classification, ontology
● 17k codes in ICD-9. 155k codes in ICD-10.
● Implicit: Disease model of medicine
28

29. My recent
Dx: Otitis
Disease vs.
Condition vs.
Symptom vs.
Phenotype
29
℅ WebMD

30. 30
IDG KMC: Gene expression vs. Tissues; Different sources, tissue terms.

31. IDG-KMC: TCRD - Target Central Research Db
+------------+------------+--------+------+------------------------------------------------------------------+--------+-------+
| doid | Disease | zscore | conf | Protein | idgfam | tdl |
+------------+------------+--------+------+------------------------------------------------------------------+--------+-------+
| DOID:13189 | Gout | 3.512 | 1.8 | Alpha-protein kinase 1 | Kinase | Tbio |
| DOID:13189 | Gout | 3.214 | 1.6 | Serine/threonine-protein kinase SIK1 | Kinase | Tchem |
| DOID:13189 | Gout | 2.922 | 1.5 | Melanocortin receptor 3 | GPCR | Tchem |
| DOID:13189 | Gout | 2.797 | 1.4 | Taste receptor type 2 member 30 | GPCR | Tbio |
| DOID:13189 | Gout | 2.576 | 1.3 | Taste receptor type 2 member 16 | GPCR | Tbio |
| DOID:13189 | Gout | 2.379 | 1.2 | Hepatocyte nuclear factor 4-gamma | NR | Tbio |
| DOID:13189 | Gout | 2.441 | 1.2 | Tyrosine-protein kinase SYK | Kinase | Tchem |
| DOID:13189 | Gout | 1.948 | 1.0 | cGMP-dependent protein kinase 2 | Kinase | Tchem |
| DOID:13189 | Gout | 1.798 | 0.9 | Pannexin-1 | IC | Tbio |
| DOID:13189 | Gout | 1.517 | 0.8 | Taste receptor type 2 member 38 | GPCR | Tbio |
| DOID:13189 | Gout | 1.565 | 0.8 | Transient receptor potential cation channel subfamily A member 1 | IC | Tclin |
| DOID:13189 | Gout | 1.531 | 0.8 | Transient receptor potential cation channel subfamily V member 1 | IC | Tclin |
| DOID:13189 | Gout | 1.388 | 0.7 | Adenosine kinase | Kinase | Tchem |
| DOID:13189 | Gout | 1.427 | 0.7 | Interleukin-1 receptor-associated kinase 1 | Kinase | Tchem |
| DOID:13189 | Gout | 1.375 | 0.7 | Transient receptor potential cation channel subfamily M member 3 | IC | Tbio |
| DOID:13189 | Gout | 1.255 | 0.6 | Free fatty acid receptor 4 | GPCR | Tchem |
| DOID:13189 | Gout | 1.231 | 0.6 | P2X purinoceptor 2 | IC | Tbio |
| DOID:13189 | Gout | 1.198 | 0.6 | Proto-oncogene tyrosine-protein kinase Src | Kinase | Tclin |
| DOID:13189 | Gout | 1.108 | 0.6 | Tribbles homolog 1 | Kinase | Tbio |
| DOID:13189 | Gout | 1.093 | 0.5 | Activin receptor type-1B | Kinase | Tchem |
| DOID:13189 | Gout | 1.048 | 0.5 | Transient receptor potential cation channel subfamily V member 2 | IC | Tbio |
+------------+------------+--------+------+------------------------------------------------------------------+--------+-------+
Disease-gene associations via literature text mining. 31

32. 32
Text mining, named entity recognition, term frequency
Natural language processing, Google, Watson, Siri, and the state of the art

33. Language Diversity of Computers
Final Thought:
“Can we talk?”*
℅ Joan Rivers, 1933-2014
33