Transcript GeneTUC: Natural Language Understanding in Medical Text PhD Defense Rune Sætre June 27th 2006

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GeneTUC: Natural Language
Understanding in Medical Text
PhD Defense
Rune Sætre
June 27th 2006
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Overview
• Motivation
• Thesis Work
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Overview (Diploma Thesis)
Idea (Paper 1 and 2)
Bioogle (Paper 3, 4 and 5)
GeneTUC (Paper 6)
• Results, Related Work and Discussion
• Comments and Questions by Jong C. Park and
Eivind Hovig
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Motivation
P ubM e d J our na l a bs t r a c t s pe r da y , by publ i c a t i on y e a r
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http://www.ncbi.nlm.nih.gov/PubMed/
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…Motivation
• Biomedical Researchers publish almost 2000
abstracts per day in MEDLINE
– Computers are needed to automatically find all (recall), and only
(precision), the relevant information
• Future Solution: GeneTUC
– TUC: The Understanding Computer
– BusTUC works for Natural Language queries about busses in
Trondheim
– GeneTUC uses full-parsing to extract knowledge from MEDLINE
– After parsing the input, GeneTUC can answer simple questions
about protein and gene interactions and other facts from the text
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Challenge: Medical language
• Example Input Sentences:
– Subsequently, activated CREB activates transcription of genes
essential for proper germ cell differentiation.+
– Indeed, Ca2+/calmodulin binds a complex of RGS4 and
a transition state analog of Galpha i1-GDP-AlF4-.*
• Medical language is not always natural language:
– Complex grammar
– Invention of new words/names every day
+ PMID:
11988318
* BioCreative1 Example, PMID 11988318
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GeneTUC Research Overview
Q
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GeneTUC
Project
NLU
review
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2001
2006
GeneTUC
Diploma
Thesis
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Time
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Legend:
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Title
Presented in Part II as research paper #n
Presented in seperate PhD Research School document
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Thesis Work
• GeneTUC Diploma Work
– Literature Review: NLU in Medicine
– GeneTUC: Full-parsing of MEDLINE Abstracts
• PhD Papers:
1 Unitex: Local Grammars
2 ProtChew: Automatic Protein Name Recognition
3 Alchymoogle: Automatic Entity Annotation
4 gProt: Automatic Protein Interaction Annotation
5 WebProt: Online gProt Experiments
6 GeneTUC: GENIA corpus experiments
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TUC Introduction
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Chat-80, Prat-89, HSQL
1991: The Understanding Computer
1996: BusTUC (atb.no/bussorakelet/)
2000: GeneTUC, diploma project
2001-2006: GeneTUC has been my PhD-Project 
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GeneTUC System
Architecture
GeneTUC
Query
MEDLINE
HG NC
• MEDLINE: Abstracts
• GO: GeneOntology
• TUC: The
GO
Understanding
Answer
Computer
• DB: TQL DataBase
TUC
DB
• HGNC: HUGO Gene
Nomenclature
Committee
• WordNet: Ontology
WordNet
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WordNet 2.0
• Online lexical reference system
– Nouns, verbs, adjectives and adverbs
• Inspired by psycholinguistic theories of human lexical memory
– Organized into synonym sets, each representing one underlying lexical
concept
• Different relations link the synonym sets
– E.g. hypernyms, hyponyms, holonyms, synonyms, coordinate terms,
domain,
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Nomenclature, HUGO
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HUGO Gene Nomenclature Committee
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Approve a gene name and symbol for each known human gene
Stored in the Human Gene Nomenclature Database
Approved 13,000 symbols (20-30,000 human genes)
Each symbol is unique
Each gene is only given one approved gene symbol
Similar names used, e.g. in mouse gene research
Efforts are made to use a symbol acceptable to workers in the field
Facilitates electronic data retrieval from publications
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Gene Ontology
• Heterarchy
– Molecular Terms
• Controlled Vocabulary
• Function, Process and Location
GO
Molecular
Function
Biological
Process
Cellular
Component
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GeneTUC Parser
S
• Top-Down, left to right
• Greedy Heuristics
• Semantic Constraints
NP
VP
– Interact(Agent: RGS4)
– The rock grows
N
PP V
PP
P
N
Rgs4 interacts with calmodulin
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Screenshot Example
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E: rgs4 interacts with calmodulin.
........................................................................
% TQL:
rgs4 isa protein
calmodulin isa protein
interact/rgs4/sk(1)
srel/with/thing/calmodulin/sk(1)
event/real/sk(1)
........................................................................
E: calmodulin interacts with cck.
........................................................................
% TQL:
cck isa gene
interact/calmodulin/sk(3)
srel/with/thing/cck/sk(3)
event/real/sk(3)
........................................................................
RGS4
Calmodulin
CCK
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Screenshot Example ctd.
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E: does rgs4 interact with cck?
...............................................................
% TQL:
[test::(rgs4 isa protein,
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cck isa gene,
interact/rgs4/A,
srel/with/thing/cck/A,
event/real/A)]
................................................................
Yes
................................................................
A transitive rule
– ProteinA interacts with ProteinB and
ProteinB interacts with ProteinC
==> ProteinA interacts with ProteinC
Calmodulin
RGS4
Calmodulin
CCK
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Dictionary
• GeneTUC does not perform very well
without a complete dictionary
• Current Solution: Bioogle can build a
dictionary
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Bioogle (Paper III)
• Current ontology: 275 medical terms
• Connect Unknowns to these Concepts
• Query syntax
– “ Unknown is (an|a) “
• Parse results until a hit is found (or not)
– “Pentagastrin is a synthetic peptide containing the five terminal amino
acids of gastrin.”
• Result: 104 of 200 terms were correctly classified
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Relations:
GeneTUC Ontology
AKO
Is-A
Thing
Set
Compound
Activity
Family
Has_A
Substance
Gastrin
Hormone
Peptide
Pentagastrin
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Google API Search
Google search
• 1000 queries per user pr day
• Free to use for everybody
• Can be programmed with SOAP in most languages
– Simple Object Access Protocol
• Results are handled automatically
• Alexa (Amazon) has implemented a similar service*
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$1 per processor hour
$1 per gigabyte/year of user storage
$1 per 50 gigabytes of data processed
$1 per gigabyte uploaded/downloaded
* http://news.bbc.co.uk/1/hi/technology/4530978.stm
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Paper IV: gProt
• What about protein interactions?
• Protein Interaction
– Protein  Protein
– BioCreAtIvE1: Protein  Set of GeneOntology Terms
• Find publicly known interactions for a given protein,
using Google as the main source for new knowledge
– Query: “ proteinX VerbY “
– Example: “ Gastrin activates “
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Paper
IV:
gProt
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Gastrin activates nuclear factor {kappa}B (NF{kappa}B) through a ...
Conclusions: Gastrin activates NF {kappa} B via a PKC dependent pathway which
involves I {kappa} B kinase, NF {kappa} B inducing kinase, and TRAF6. ...
gut.bmjjournals.com/cgi/content/abstract/52/6/813 - Lignende sider
Gastrin activates nuclear factor {kappa}B (NF{kappa}B) through a ...
gut.bmjjournals.com/cgi/reprint/52/6/813 - Lignende sider
Gastrin activates nuclear factor kappaB (NFkappaB) through a ...
BACKGROUND: We previously reported that gastrin induces expression of CXC chemokines
through activat...
www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve& db=PubMed&list_uids=12740336&dopt=Abstract Lignende sider
Gastrin activates nuclear factor kappaB (NFkappaB) through a ...
CONCLUSIONS: Gastrin activates NFkappaB via a PKC dependent pathway which involves
IkappaB kinase, NFkappaB inducing kinase, and TRAF6. MeSH Terms: ...
www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve& db=PubMed&list_uids=12740336&dopt=Citation Lignende sider
[ Flere resultater fra www.ncbi.nlm.nih.gov ]
Gastrin activates nuclear factor kappaB (NFkappaB) through a ...
iHOP - Information Hyperlinked over Proteins · Gastrin activates nuclear factor
kappaB (NFkappaB) through a protein kinase C dependent pathway involving ...
www.pdg.cnb.uam.es/UniPub/iHOP/gp/9705030.html - 7k - I hurtigbuffer - Lignende sider
Gast - Gastrin precursor
Gastrin activates rat stomach histidine decarboxylase via cholecystokinin-B/gastrin
receptors. Abstract-863492. Gastrin activated transcription through a ...
www.pdg.cnb.uam.es/UniPub/iHOP/gg/121191.html - 105k - I hurtigbuffer - Lignende sider
[ Flere resultater fra www.pdg.cnb.uam.es ]
Anatomy & Physiology Lecture Outlines
aa. gastrin activates gastric juice secretion & gastric smooth muscle “churning” bb.
gastrin activates gastroileal reflex which moves chyme from ileum to ...
www.gwc.maricopa.edu/class/bio202/digestlc.htm - 20k - I hurtigbuffer - Lignende sider
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Paper IV: gProt
• Results, 2000 facts
Errors
26 %
Proteins
17 %
GO-terms
57 %
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Paper V: WebProt
• Online Implementation, bigger experiment
• Can Annotate Protein Interactions with 70% precision
• Tested the effect of source filtering
– 90% precision, but recall dropping to 70%
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Google as a source
nih.gov
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PubMed Central Collection of Journals, Books and MEDLINE
jbc.org
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Biological Chemistry, Journal
physiology.org
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American Physiological Society, Collection of Journals
endojournals.org
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Endocrine Society, Collection of Journals
asm.org
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American Society for Microbiology, Collection of Journals
ahajournals.org
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American Heart Association, Collection of Journals
nature.com
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Nature, same as npgjournals.com, Collection of Journals
ingentaconnect.com
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Ingenta Online Publisher, Collection of Journals
aacrjournals.org
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Cancer Research Journal
jimmunol.org
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Immunology, Journal
karger.com
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Karger Medical and Scientific Publisher, Big Collection of Journals
pnas.org
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National Academy of Sciences USA, Proceedings
ac.uk
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MOLECULAR AND CELLULAR BIOLOGY, Journal
bloodjournal.org
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American Society of Hematology, Blood Journal
uam.es
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Information Hyperlinked over Proteins (iHOP), Network
aspetjournals.org
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Molecular Pharmacology Journal
oxfordjournals.org
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Human Molecular Genetics Journal
blackwell-synergy.com
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Neurochemistry, Journal
jcb.org
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Cell Biology, Journal
biochemj.org
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Biochemical Journal
npgjournals.com
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Collection including European Molecular Biology Organization Journal
1480
4660 facts
total from
WebProt
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WebProt
Percent
F-measure
100,0 %
90,0 %
80,0 %
70,0 %
60,0 %
50,0 %
40,0 %
30,0 %
20,0 %
10,0 %
0,0 %
Precision
Recall
F-measure
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Hit Limit
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Screenshot
WebProt
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Paper VI: GeneTUC Results
• Can parse 60% of test input sentences in the GENIA
corpus (500 abstracts),
– With 86% accuracy on the POS-tagging
– Bracketing Precision and Recall scores of 70,6% and 53,9%
• And answer simple questions about the parsed
sentences
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Evalb scores
Sent.
Matched
Paper VI
Bracket
Cross
Correct-Tag
Len.
Recall
Prec.
Bracket
gold
test
Bracket
Words
Tags
Accuracy
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73.33
91.67
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15
12
0
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88.24
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60.00
75.00
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10
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12
100.00
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69.23
90.00
9
13
10
0
15
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86.67
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40.00
57.14
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10
7
1
14
12
85.71
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40.00
58.82
10
25
17
3
29
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86.21
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14.29
16.67
1
7
6
3
12
10
83.33
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22.22
40.00
4
18
10
0
20
17
85.00
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18.18
25.00
4
22
16
9
23
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86.96
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51.61
69.57
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23
2
32
28
87.50
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13.33
14.29
2
15
14
7
23
15
65.22
40.36
54.47
67
166
123
4
197
167
84.77
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Summary
• 6 papers describing the steps needed to show that
GeneTUC can handle medical text
• 60% parsing success-rate may not be enough for a
commercial application,
– But the fact that it improved from just 10% in 2001 is very promising
• Once the parsing success-rate is good enough,
GeneTUC can be tested on Question-Answering
– There is a need for a good public dataset that allows measuring
and comparing between different QA systems (Future Work)
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Acknowledgements
• Biologists:
– Astrid Lægreid, Kamilla Stunes, Kristine Misund, Liv Thommesen,
Tonje Strømmen Steigedal
• Computer Scientists:
– Tore Amble, Arne Halaas, Amund Tveit, Martin Ranang, Harald
Søvik, Yoshimasa Tsuruoka, Anders Andenæs, Tor-Kristian
Jenssen, Franz Günthner, Jun’ichi Tsujii, Jörg Cassens, Waclaw
Kusnierczyk, Tore Bruland, Peep Küngas, Magnus Lie Hetland,
Morten Hartman, Hallgeir Bergum, Jo Kristian Bergum, Frode
Jünge, Heri Ramampiaro, Rolv Inge Seehuus, Per Kristian Lehre,
Clemens Marschner, Petra Maier, Holger Bosk, Sebastian Nagel,
Mariya Vitusevych, Yoshimasa Tsuruoka, Jin-Dong Kim, Hong-Woo
Chun, Takashi Ninomiya, Yusuke Miyao, Frode Høyvik, Henrik
Tveit, Jian Su and others
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Questions and Comments
• Associate professor Jong C. Park
– Computer Science Division,
– Korea Advanced Institute of Science and Technology
(KAIST),
– Daejeon, South Korea
• Professor Eivind Hovig
– Department of Tumor Biology,
– Institute for Cancer Research,
– The Norwegian Radium Hospital
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Thesis Work
• GeneTUC Project
– Use TUC in the Medical Text Domain
• Use Google (Bioogle) to Recognize Unknown Entities
– Galpha(i1)-GDP-AlF(4)(-), Ca2+, Gastrin
• Use Google (WebProt) to do Automatic Annotation
– Mapping (BioCreative):
• From Gene/Protein  Set of GeneOntology Terms
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Motivation
• Natural language is natural 
– Talking computers
– Voice as input
• Repetitive tasks should be automated!
– Information Extraction is trivial,
if you know what to look for
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0: GeneTUC Diploma Work
• NLU Review 2002
– GENIA: HPSG
– Park et al.: CCG-parsing
• Numbers?
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Paper I: Local Grammars
• Maurice Gross:
– there is more than 10^50 ways to build a sentence with at most
twenty words*
* Gross (1997). Construction of Local Grammars
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Paper II: ProtChew
• Protein Names
– Galpha(i1)-GDP-AlF(4)(-)
– Gastrin
– …
• Idea: Automatic Extraction
– Based on existing dictionaries and machine learning
• Results?
÷
Protein-related
Tokens
Part of Protein
Name Tokens
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evalb
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[4] OUTPUT FORMAT FROM THE SCORER
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The scorer gives individual scores for each sentence, for
example:
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Sent.
Matched Bracket Cross
Correct Tag
ID Len. Stat. Recal Prec. Bracket gold test Bracket
Words Tags Accracy
============================================
=====
1 8 0 100.00 100.00 5
5 5
0
6 5
83.33
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At the end of the output the === Summary === section
gives statistics
for all sentences, and for sentences <=40 words in length.
The summary
contains the following information:
i) Number of sentences -- total number of sentences.
ii) Number of Error/Skip sentences -- should both be 0 if
there is no
problem with the parsed/gold files.
iii) Number of valid sentences = Number of sentences Number of Error/Skip
sentences
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iv) Bracketing recall = (number of correct constituents)
---------------------------------------(number of constituents in the goldfile)
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v) Bracketing precision = (number of correct constituents)
---------------------------------------(number of constituents in the parsed file)
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vi) Complete match = percentaage of sentences where
recall and precision are
both 100%.
vii) Average cross=(#const crossing a goldfile constituen
---------------------------------------(number of sentences)
viii) No crossing = percentage of sentences which have 0
crossing brackets.
ix) 2 or less crossing = percentage of sentences which
have <=2 crossing brackets.
x) Tagging accuracy = percentage of correct POS tags
(but see [5].3 for exact
details of what is counted).
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Remember
• Present one paper at the time
• Summary results and related work also in the end
Ta med tabeller for parsing, sammenligning
med andre etc.
Et eksempel på en kompleks setning med gtb
treet. Ref tabell. Sammenlign brackets.
Ta med webprot screenshot
Related work!! Phd pres.
Related work. Lexiquest, 40 verbs, hva er fscore?
Fra tore:
Hvorfor bare 50%. Er det semantikk eller
gramatikk som gjør at 50% feiler
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Dr. Carl-Fredrik Sørensen (50 min, jeg: tid /2)
5 min intro, state-of-the-art
5 min definitions NLU
10 min thesis/papers overview and Research Questions
15 min three themes and contributions. Evaluation of the work
10 min future work
Proof of concept. It can be implemented. Next step?
Industry... Results are trusted
Academic... Results are validated through understanding the
research process.
Dennings, proof of concept
Research question...soon....Moores law
Proof of performance
Shift the work to biologists
Medline growth graph.
Figure... Everything is published.
Background:
http://www.coli.uni-saarland.de/~hansu/what_is_cl.html
Schopenhauer: imagine how clever a vice man would be, if he
knew everything in his books.
Inter-annotator agreement in gprot, maybe 80 percent precision
is enough?!