To access the content of digital texts efficiently, it is necessary to provide more sophisticated access than keyword based searching. GeneScene provides biomedical researchers with research findings and background relations automatically extracted from text and experimental data. These provide a more detailed overview of the information available. The extracted relations were evaluated by qualified researchers and are precise. A qualitative ongoing evaluation of the current online interface indicates that this method to search the literature is more useful and efficient than keyword based searching

Chen, Hsinchun

Eggers, Shauna

Falsey, Ryan R.

Huang, Zan

Kislin, Kerri L.

Leroy, Gondy

Li, Jiexun

Martinez, Jesse D.

McDonald, Daniel M.

Ng, Gavin

Xu, Jie

English

Scholarship@Claremont

Claremont CollegesScholarship @ ClaremontCGU Faculty Publications and Research CGU Faculty Scholarship5-1-2003Genescene: Biomedical Text and Data MiningGondy LeroyClaremont Graduate UniversityHsinchun ChenUniversity of ArizonaJesse D. MartinezUniversity of ArizonaShauna EggersUniversity of ArizonaRyan R. FalseyUniversity of ArizonaSee next page for additional authorsThis Conference Proceeding is brought to you for free and open access by the CGU Faculty Scholarship at Scholarship @ Claremont. It has beenaccepted for inclusion in CGU Faculty Publications and Research by an authorized administrator of Scholarship @ Claremont. For more information,please contact scholarship@cuc.claremont.edu.Recommended CitationLeroy, G.; Hsinchun Chen; Martinez, J.D.; Eggers, S.; Falsey, R.R.; Kislin, K.L.; Zan Huang; Jiexun Li; Jie Xu; McDonald, D.M.; Ng,G., "Genescene: biomedical text and data mining," Digital Libraries, 2003. Proceedings. 2003 Joint Conference on , vol., no.,pp.116,118, 31-31 May 2003 doi: 10.1109/JCDL.2003.1204906AuthorsGondy Leroy, Hsinchun Chen, Jesse D. Martinez, Shauna Eggers, Ryan R. Falsey, Kerri L. Kislin, Zan Huang,Jiexun Li, Jie Xu, Daniel M. McDonald, and Gavin NgThis conference proceeding is available at Scholarship @ Claremont: http://scholarship.claremont.edu/cgu_fac_pub/120Genescene: Biomedical Text And Data Mining   Gondy Leroy1, Hsinchun Chen1, Jesse D. Martinez2, Shauna Eggers1, Ryan R. Falsey2, Kerri L. Kislin2, Zan Huang1, Jiexun Li1, Jie Xu1, Daniel M. McDonald1, Gavin Ng1  Management Information Systems1, Arizona Cancer Center2 The University of Arizona gleroy@eller.arizona.edu; hchen@eller.arizona.edu; jmartinez@azcc.arizona.edu; seggers@email.arizona.edu; rfalsey@u.arizona.edu; kkislin@u.arizona.edu; zhuang@eller.arizona.edu; jiexun@eller.arizona.edu; jxu@eller.arizona.edu; dmm@eller.arizona.edu; tgavinng@ai.bpa.arizona.edu  Abstract  To access the content of digital texts efficiently, it is necessary to provide more sophisticated access than keyword based searching. Genescene provides biomedical researchers with research findings and background relations automatically extracted from text and experimental data. These provide a more detailed overview of the information available. The extracted relations were evaluated by qualified researchers and are precise. A qualitative ongoing evaluation of the current online interface indicates that this method to search the literature is more useful and efficient than keyword based searching.  1. Introduction  The Internet has increased the availability of and access to publications, leading in many cases to information overload. In biomedicine, this effect has been accelerated by an increase in publications and datasets due to the decoding of the human genome. Every 11 years, the number of researchers doubles [6] and Medline, the main resource of research literature, has been growing with more than 10,000 abstracts per week since 2002 [5]. In addition, large amounts of gene expression data involving expression measurements of thousands of genes, are available from microarray experiments. Gene expression patterns embedded in the data can potentially lead to the discovery of unknown genetic relations.  The information overload problem for both literature and data analysis calls for solutions that can largely automate these processes. Genescene, a toolkit developed for biomedicine, will provide more adequate access to text and data. It will allow researchers to view the findings extracted from Medline abstracts, and compare them with findings from microarray experiments.    2. Related Work  To access the content of documents, natural language processing (NLP) is required. Existing techniques range from simple pattern matching to full parsers. In biomedicine, many NLP approaches start from a list with specific gene names or verbs and extract the surrounding text as relations [2; 7; 8]. The best among these achieve high precision but low recall since few relations are extracted. More general co-occurrence based approaches assume that phrases, e.g., genes, are related when they appear together in a text [3]. This approach extracts more relations but they are less precise. Both methods ignore negation.  3. Genescene  Genescene combines relations between entities in text extracted by a rule-based parser and a corpus-based co-occurrence analysis technique. It will also extract regulatory relations from microarray data and combine these with the relations extracted from text.  3.1. Rule-based findings  The rule-based parser, successfully tested with a small prototype [4], is based on closed-class words which provide a generic structure for the relations. Cascaded finite state automata (FSA), built around prepositions and basic sentence elements, identify the structures. Each FSA incorporates negation, an important element ignored by others, and also captures relations based on conjunctions. For example, from the sentence “Thus hsp90 does not inhibit receptor function solely by… ,” the relation “NOT: hsp90  – inhibit – receptor function” is extracted.      3.2. Corpus-based background  The corpus-based background relations represent the knowledge in the entire domain and form the background for the rule-based relations. These corpus-based relations are formed by a co-occurrence-based algorithm tested earlier in an information retrieval context [1]. They represent relations between noun phrases that hold true for the entire collection.  3.3. Ontologies  Three ontologies, the Gene Ontology (GO), the Human Genome (HUGO) Nomenclature, and the Unified Medical Language System (UMLS), are used to better integrate the relations. A term can receive multiple semantic tags based on its being in the ontologies.  3.4. Regulatory relations from data  Regulatory relations are extracted from microarray data using data mining techniques such as Bayesian networks and association rule mining. Incorporating these regulatory relations into Genescene will help researchers compare experimental discovery with previous knowledge from literature. Unexpected gene associations can be identified to guide further literature search or new experimental design.  3.5. Online Access  Users can currently access Genescene’s text mining demo (http://ai.bpa.arizona.edu/go/genescene). Keywords are used to retrieve relations found in the Medline abstracts and users can choose the type of relation they want to see. A list of relations is then shown for the search, ordered by the type and number of elements in the relation. The number of abstracts containing the relations is also shown. Clicking on a relation retrieves the list of associated abstracts. The relations and search terms are highlighted in the abstract text.  4. Case study  The extracted relations and online interface are being evaluated with quantitative and qualitative studies.  4.1. Genescene relations  Two researchers evaluated the rule- and corpus-based relations from p53-related abstracts. The rule-based relations were 95% correct and the corpus-based relations were 60% correct. Limiting the corpus-based relations to those that had entities with ontology tags increased their correctness to 78%. Most terms and relations were considered relevant, especially when part of an ontology.  4.2. Genescene content  Encouraged by the excellent evaluation results, three collections with biomedical abstracts were added to Genescene. Different research groups requested the collections. Table 1 provides an overview. The P53 collection contains all abstracts available in Medline (up to and including summer 2002) with the keyword “p53” in the title or abstract. The AP1 collection is similarly based on the keywords: ap1, ap-1, jnk, erk, jun, fos, and p38. The yeast collection is based on the keyword “yeast.” Hundreds of thousands of relations are available for each collection. In each, more than half of the terms received a tag from an ontology. The UMLS provided more than 50% of the terms with a tag. Slightly more than 1% of the terms received a GO-tag. HUGO provided the least tags, except for the yeast collection where 1.4% of the terms received a HUGO tag.  Table 1. Overview of Genescene’s content Topic: P53 AP1 Yeast Abstracts: 23,234 30,820 56,246 Rule-B. Relations: 270,008 387,666 560,165 Corpus-B.  Relations: 5,023,103 6,526,454 7,736,647 Terms w. UMLS tag: 57% 54% 51% Terms w.  HUGO tag: 0.6% 0.9% 1.4% Terms w. GO tag: 1.1% 1.1% 1.1% Any ontology tag: 58% 55% 52%  4.3. Genescene interface  The ordering of the relations was very much liked. Researchers felt the most important relations, e.g., conclusions, were presented first. They also liked the highlighting of relations and keywords in the original abstracts. However, currently only one keyword or phrase can be used to search a collection. A multiple-keyword search should be added and the speed of partial match queries for background relations should be improved.  5. Future Directions  In the future, we will integrate both the rule- and corpus-based relations in an interactive graphical map display. Later, visual text mining will become possible. For example, users will be able to search for specific time ranges in the publications or for specific organism. Furthermore, findings from text will be incorporated into microarray data mining results to improve the algorithmic performance and vice versa microarray findings will lead to automatic literature searches.  6. Acknowledgements  Genescene is supported by the grant: NIH/NLM, 1 R33 LM07299-01, 2002-2005, “Genescene: a Toolkit for Gene Pathway Analysis.” We thank the National Library of Medicine, the Gene Ontology Consortium, and the Hugo Nomenclature Committee for making the ontologies available to researchers.  7. References  [1] Chen, H. and Lynch, K. J. Automatic Construction of Networks of Concepts Characterizing Document Databases. IEEE Transactions on Systems, Man and Cybernetics, 22, 5 (1992), 885-902.  [2] Friedman, C., Kra, P., Yu, H., Krauthammer, M. and Rzhetsky, A. GENIES: a natural-language processing system for the extraction of molecular pathways from journal articles. Bioinformatics, 17, Suppl. 1 (2001), S74-S82.  [3] Jenssen, T.-K., Laegreid, A., Komorowski, J. and Hovig, E. A literature network of human genes for high-throughput analysis of gene expression. Nature Genetics, 28, (2001), 21-28.  [4] Leroy, G. and Chen, H. Filling Preposition-based Templates to Capture Information from Medical Abstracts. Paper presented at the Pacific Symposium on Biocomputing, (2002), 350-361.  [5] National-Library-of-Medicine, Fact Sheet - Medline. National Library of Medicine: http://www.nlm.nih.gov/pubs/factsheets/medline.html  [6] Perutz, M. F. Will biomedicine outgrow support? Nature, 399, (1999), 299-301.  [7] Pustejovsky, J., Castaño, J., Zhang, J., Kotecki, M. and Cochran, B. Robust Relational Parsing Over Biomedical Literature: Extracting Inhibit Relations. Paper presented at the Pacific Symposium on Biocomputing (2002), 362-373.  [8] Thomas, J., Milward, D., Ouzounis, C., Pulman, S. and Carroll, M. Automatic Extraction of Protein Interactions from Scientific Abstracts. Paper presented at the Pacific Symposium on Biocomputing (2000), 538-549.    

Genescene: Biomedical Text and Data Mining

To access the content of digital texts efficiently, it is necessary to provide more sophisticated access than keyword based searching. Genescene provides biomedical researchers with research findings and background relations automatically extracted from text and experimental data. These provide a more detailed overview of the information available. The extracted relations were evaluated by qualified researchers and are precise. A qualitative ongoing evaluation of the current online interface indicates that this method to search the literature is more useful and efficient than keyword based searching. 1

Gondy Leroy

Hsinchun Chen

Jesse D. Martinez

Shauna Eggers

Ryan R. Falsey

Kerri L. Kislin

Zan Huang

Jiexun Li

Jie Xu

Daniel M. Mcdonald

Gavin Ng

CiteSeerX

Genescene: biomedical text and data mining

Keck Graduate Institute

Claremont Colleges
Scholarship @ Claremont
CGU Faculty Publications and Research CGU Faculty Scholarship
5-1-2003
Genescene: Biomedical Text and Data Mining
Gondy Leroy
Claremont Graduate University
Hsinchun Chen
University of Arizona
Jesse D. Martinez
University of Arizona
Shauna Eggers
University of Arizona
Ryan R. Falsey
University of Arizona
See next page for additional authors
This Conference Proceeding is brought to you for free and open access by the CGU Faculty Scholarship at Scholarship @ Claremont. It has been
accepted for inclusion in CGU Faculty Publications and Research by an authorized administrator of Scholarship @ Claremont. For more information,
please contact scholarship@cuc.claremont.edu.
Recommended Citation
Leroy, G.; Hsinchun Chen; Martinez, J.D.; Eggers, S.; Falsey, R.R.; Kislin, K.L.; Zan Huang; Jiexun Li; Jie Xu; McDonald, D.M.; Ng,
G., "Genescene: biomedical text and data mining," Digital Libraries, 2003. Proceedings. 2003 Joint Conference on , vol., no.,
pp.116,118, 31-31 May 2003 doi: 10.1109/JCDL.2003.1204906
Authors
Gondy Leroy, Hsinchun Chen, Jesse D. Martinez, Shauna Eggers, Ryan R. Falsey, Kerri L. Kislin, Zan Huang,
Jiexun Li, Jie Xu, Daniel M. McDonald, and Gavin Ng
This conference proceeding is available at Scholarship @ Claremont: http://scholarship.claremont.edu/cgu_fac_pub/120
Genescene: Biomedical Text And Data Mining 
 
 
Gondy Leroy1, Hsinchun Chen1, Jesse D. Martinez2, Shauna Eggers1, Ryan R. Falsey2, 
Kerri L. Kislin2, Zan Huang1, Jiexun Li1, Jie Xu1, Daniel M. McDonald1, Gavin Ng1  
Management Information Systems1, Arizona Cancer Center2 
The University of Arizona 
gleroy@eller.arizona.edu; hchen@eller.arizona.edu; jmartinez@azcc.arizona.edu; 
seggers@email.arizona.edu; rfalsey@u.arizona.edu; kkislin@u.arizona.edu; 
zhuang@eller.arizona.edu; jiexun@eller.arizona.edu; jxu@eller.arizona.edu; 
dmm@eller.arizona.edu; tgavinng@ai.bpa.arizona.edu
 
 
Abstract 
 
To access the content of digital texts efficiently, it is 
necessary to provide more sophisticated access than 
keyword based searching. Genescene provides biomedical 
researchers with research findings and background 
relations automatically extracted from text and 
experimental data. These provide a more detailed 
overview of the information available. The extracted 
relations were evaluated by qualified researchers and are 
precise. A qualitative ongoing evaluation of the current 
online interface indicates that this method to search the 
literature is more useful and efficient than keyword based 
searching. 
 
1. Introduction 
 
The Internet has increased the availability of and 
access to publications, leading in many cases to 
information overload. In biomedicine, this effect has been 
accelerated by an increase in publications and datasets 
due to the decoding of the human genome. Every 11 
years, the number of researchers doubles [6] and Medline, 
the main resource of research literature, has been growing 
with more than 10,000 abstracts per week since 2002 [5]. 
In addition, large amounts of gene expression data 
involving expression measurements of thousands of 
genes, are available from microarray experiments. Gene 
expression patterns embedded in the data can potentially 
lead to the discovery of unknown genetic relations.  
The information overload problem for both literature 
and data analysis calls for solutions that can largely 
automate these processes. Genescene, a toolkit developed 
for biomedicine, will provide more adequate access to 
text and data. It will allow researchers to view the 
findings extracted from Medline abstracts, and compare 
them with findings from microarray experiments. 
 
 
 
2. Related Work 
 
To access the content of documents, natural language 
processing (NLP) is required. Existing techniques range 
from simple pattern matching to full parsers. In 
biomedicine, many NLP approaches start from a list with 
specific gene names or verbs and extract the surrounding 
text as relations [2; 7; 8]. The best among these achieve 
high precision but low recall since few relations are 
extracted. More general co-occurrence based approaches 
assume that phrases, e.g., genes, are related when they 
appear together in a text [3]. This approach extracts more 
relations but they are less precise. Both methods ignore 
negation. 
 
3. Genescene 
 
Genescene combines relations between entities in text 
extracted by a rule-based parser and a corpus-based co-
occurrence analysis technique. It will also extract 
regulatory relations from microarray data and combine 
these with the relations extracted from text. 
 
3.1. Rule-based findings 
 
The rule-based parser, successfully tested with a small 
prototype [4], is based on closed-class words which 
provide a generic structure for the relations. Cascaded 
finite state automata (FSA), built around prepositions and 
basic sentence elements, identify the structures. Each FSA 
incorporates negation, an important element ignored by 
others, and also captures relations based on conjunctions. 
For example, from the sentence “Thus hsp90 does not 
inhibit receptor function solely by… ,” the relation “NOT: 
hsp90  – inhibit – receptor function” is extracted.  
 
 
 
 
3.2. Corpus-based background 
 
The corpus-based background relations represent the 
knowledge in the entire domain and form the background 
for the rule-based relations. These corpus-based relations 
are formed by a co-occurrence-based algorithm tested 
earlier in an information retrieval context [1]. They 
represent relations between noun phrases that hold true 
for the entire collection. 
 
3.3. Ontologies 
 
Three ontologies, the Gene Ontology (GO), the Human 
Genome (HUGO) Nomenclature, and the Unified Medical 
Language System (UMLS), are used to better integrate 
the relations. A term can receive multiple semantic tags 
based on its being in the ontologies. 
 
3.4. Regulatory relations from data 
 
Regulatory relations are extracted from microarray 
data using data mining techniques such as Bayesian 
networks and association rule mining. Incorporating these 
regulatory relations into Genescene will help researchers 
compare experimental discovery with previous 
knowledge from literature. Unexpected gene associations 
can be identified to guide further literature search or new 
experimental design. 
 
3.5. Online Access 
 
Users can currently access Genescene’s text mining 
demo (http://ai.bpa.arizona.edu/go/genescene). Keywords 
are used to retrieve relations found in the Medline 
abstracts and users can choose the type of relation they 
want to see. A list of relations is then shown for the 
search, ordered by the type and number of elements in the 
relation. The number of abstracts containing the relations 
is also shown. Clicking on a relation retrieves the list of 
associated abstracts. The relations and search terms are 
highlighted in the abstract text. 
 
4. Case study 
 
The extracted relations and online interface are being 
evaluated with quantitative and qualitative studies. 
 
4.1. Genescene relations 
 
Two researchers evaluated the rule- and corpus-based 
relations from p53-related abstracts. The rule-based 
relations were 95% correct and the corpus-based relations 
were 60% correct. Limiting the corpus-based relations to 
those that had entities with ontology tags increased their 
correctness to 78%. Most terms and relations were 
considered relevant, especially when part of an ontology. 
 
4.2. Genescene content 
 
Encouraged by the excellent evaluation results, three 
collections with biomedical abstracts were added to 
Genescene. Different research groups requested the 
collections. Table 1 provides an overview. The P53 
collection contains all abstracts available in Medline (up 
to and including summer 2002) with the keyword “p53” 
in the title or abstract. The AP1 collection is similarly 
based on the keywords: ap1, ap-1, jnk, erk, jun, fos, and 
p38. The yeast collection is based on the keyword 
“yeast.” Hundreds of thousands of relations are available 
for each collection. In each, more than half of the terms 
received a tag from an ontology. The UMLS provided 
more than 50% of the terms with a tag. Slightly more than 
1% of the terms received a GO-tag. HUGO provided the 
least tags, except for the yeast collection where 1.4% of 
the terms received a HUGO tag. 
 
Table 1. Overview of Genescene’s content 
Topic: P53 AP1 Yeast 
Abstracts: 23,234 30,820 56,246 
Rule-B. Relations: 270,008 387,666 560,165 
Corpus-B.  Relations: 5,023,103 6,526,454 7,736,647 
Terms w. UMLS tag: 57% 54% 51% 
Terms w.  HUGO tag: 0.6% 0.9% 1.4% 
Terms w. GO tag: 1.1% 1.1% 1.1% 
Any ontology tag: 58% 55% 52% 
 
4.3. Genescene interface 
 
The ordering of the relations was very much liked. 
Researchers felt the most important relations, e.g., 
conclusions, were presented first. They also liked the 
highlighting of relations and keywords in the original 
abstracts. However, currently only one keyword or phrase 
can be used to search a collection. A multiple-keyword 
search should be added and the speed of partial match 
queries for background relations should be improved. 
 
5. Future Directions 
 
In the future, we will integrate both the rule- and 
corpus-based relations in an interactive graphical map 
display. Later, visual text mining will become possible. 
For example, users will be able to search for specific time 
ranges in the publications or for specific organism. 
Furthermore, findings from text will be incorporated into 
microarray data mining results to improve the algorithmic 
performance and vice versa microarray findings will lead 
to automatic literature searches. 
 
6. Acknowledgements 
 
Genescene is supported by the grant: NIH/NLM, 1 
R33 LM07299-01, 2002-2005, “Genescene: a Toolkit for 
Gene Pathway Analysis.” We thank the National Library 
of Medicine, the Gene Ontology Consortium, and the 
Hugo Nomenclature Committee for making the ontologies 
available to researchers. 
 
7. References 
 
[1] Chen, H. and Lynch, K. J. Automatic Construction of 
Networks of Concepts Characterizing Document Databases. 
IEEE Transactions on Systems, Man and Cybernetics, 22, 5 
(1992), 885-902. 
 
[2] Friedman, C., Kra, P., Yu, H., Krauthammer, M. and 
Rzhetsky, A. GENIES: a natural-language processing system for 
the extraction of molecular pathways from journal articles. 
Bioinformatics, 17, Suppl. 1 (2001), S74-S82. 
 
[3] Jenssen, T.-K., Laegreid, A., Komorowski, J. and 
Hovig, E. A literature network of human genes for high-
throughput analysis of gene expression. Nature Genetics, 28, 
(2001), 21-28. 
 
[4] Leroy, G. and Chen, H. Filling Preposition-based 
Templates to Capture Information from Medical Abstracts. 
Paper presented at the Pacific Symposium on Biocomputing, 
(2002), 350-361. 
 
[5] National-Library-of-Medicine, Fact Sheet - Medline. 
National Library of Medicine: 
http://www.nlm.nih.gov/pubs/factsheets/medline.html 
 
[6] Perutz, M. F. Will biomedicine outgrow support? 
Nature, 399, (1999), 299-301. 
 
[7] Pustejovsky, J., Castaño, J., Zhang, J., Kotecki, M. 
and Cochran, B. Robust Relational Parsing Over Biomedical 
Literature: Extracting Inhibit Relations. Paper presented at the 
Pacific Symposium on Biocomputing (2002), 362-373. 
 
[8] Thomas, J., Milward, D., Ouzounis, C., Pulman, S. 
and Carroll, M. Automatic Extraction of Protein Interactions 
from Scientific Abstracts. Paper presented at the Pacific 
Symposium on Biocomputing (2000), 538-549. 
 
 
 


Artificial Intelligence Lab, Department of MIS, University of ArizonaTo access the content of digital texts efficiently, it is necessary to provide more sophisticated access than keyword based searching. Genescene provides biomedical researchers with research findings and background relations automatically extracted from text and experimental data. These provide a more detailed overview of the information available. The extracted relations were evaluated by qualified researchers and are precise. A qualitative ongoing evaluation of the current online interface indicates that this method to search the literature is more useful and efficient than keyword based searching

The University of Arizona

Genescene: Biomedical Text And Data Mining

To access the content of digital texts efficiently, it is necessary to provide more sophisticated access than keyword based searching. Genescene provides biomedical researchers with research findings and background relations automatically extracted from text and experimental data. These provide a more detailed overview of the information available. The extracted relations were evaluated by qualified researchers and are precise. A qualitative evaluation of the current online interface indicates that this form of searching is more useful and efficient than keyword based searching

Ryan Falsey

Daniel Mcdonald

http://arizona.openrepository.com/arizona/bitstream/10150/105791/1/leroy4.pdf

Genescene: Biomedical Text and Data Mining

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