Research Survey: Automatic
Hypothesis Generation using
Literature-Based Discovery
Kathleen Padova, June 2015
Undiscovered Public Knowledge
 Don R. Swanson (1924 – 2012)
 Concern over increased domain specialization [1]
 Focused on computer-aided information retrieval
 Uncovering unseen links between two distinct areas of
study (aka disjoint literatures) could yield new discoveries
a.k.a. “undiscovered public knowledge.”
 Pioneered the field of literature-based discovery (LBD)
ABCs of Literature-based
Discovery
 ABC Method
?
A->B
 Open vs. Closed
+
B->C
=
A->C
“Fish Oil and Reynaud’s Disease”
 Swanson hypothesized a connection between
dietary fish oil and Reynaud’ Syndrome
 1986 - Two papers on same topic:
 Library Quarterly [1]
 Perspectives in Biology and Medicine [2]
 Validated 3 years later in clinical trials [3]
 1989 - Magnesium Deficiency and Migraines, also
supported later by clinical trials [4]
Search Process
Literature on:
Literature:
Migraine
Vascular Reactivity
Spreading Depression
Calcium Channel
Blockers
Inflammation
Prostaglandins
Platelet Aggregation
Serotonin
Cerebral Anoxia
Epilepsy
Literature:
Magnesium
Image reproduced from [4]
ARROWSMITH

Previous LBD studies were “partially systematic”

Swanson joined with Neil R. Smalheiser, Department of Psychiatry, University of
Illinois

Together they developed a set of interactive software and database search
strategies to facilitate discovery [5]

Available: http://arrowsmith.psych.uic.edu/cgi-bin/arrowsmith_uic/start.cgi

Applications:
 Assessing a gap in the biomedical literature: Magnesium deficiency and neurologic



disease. Neuroscience Research Communications [6]
Indomethacin and Alzheimer's Disease. Neurology [7]
Linking Estrogen to Alzheimer's Disease: An informatics approach. Neurology [8]
Calcium-independent phospholipase A-sub2 and schizophrenia. Archives of General
Psychiatry [9]
ARROWSMITH
AB
AB1
B1
B1C
AB2
Target
Literature
A
B2
B2C
AB3
B3
B3C
AB4
B4
B4C
Source
Literature
C
Intermediate Literatures
Image reproduced from [5]
Basic LBD methodology
 Information/Entity Retrieval/Extraction
 Query the literature for areas of interest
 Identify key concepts/terms
 Characterize the literature
 Hypothesis Generation
 Find connections between retrieved literatures
 Evaluation / Vetting
 Review connections for novelty, feasibility
Information/Entity
Retrieval/Extraction
 Swanson’s method very manual
 Query formulation essential [10]
 As literature grew, search results became overwhelming
 Further research attempted to resolve by use of:
 Controlled Vocabularies [11][12]
 Established Subject Headings/Ontologies (e.g. MeSH) [13][14]
[15]
 Text mining techniques incl. ranking, weighting, clustering [16]
 Information modeling [17][18]
 Relationship Extraction[19]
Hypothesis Generation
 Early LBD relied on manually creating a 2nd query and
co-occurrence of query terms
 Later research improvements include:







Semantics/NLP to extract relationships [20][21][22]
Latent Semantic indexing [23]
Vector Space Modeling [24][25][26][27]
Lexical Statistics [28]
Fuzzy Set Theory [29][30]
Baysian Nets [31]
Ranking, weighting [14][32][33]
Hypothesis Evaluation / Vetting
 Dependent on domain expert review
 Results typically in lists and tables, long to review
 Later LBD methods include ranking and more recently,
visualizations [34][35]
 Still requires a domain expert; but evaluation is easier
Beyond ABC
 Discovery patterns [27]
 Multiple intermediary steps [36]
Applications
 LBD seen largely in biomedical sciences (mining
MEDLINE, PubMed, newer Gene dbs)
 Drug repositioning [37]
 Drug-Disease linkages [38]
 Gene Disease linkages [39][40][41]
 A few non-medical science applications
 Water purification [42]
 Technology and social issues [43]
References
[1] D. R. Swanson, “Undiscovered Public Knowledge,” Libr. Q., vol. 56, no. 2, pp. 103–118, 1986.
[2] D. R. Swanson, “Fish oil, Raynaud’s syndrome, and undiscovered public knowledge.,” Perspect. Biol. Med., vol. 30, no. 1,
pp. 7–18, 1986.
[3] R. A. DiGiacomo, J. M. Kremer, D. M. Shah, M. D. Albany, and N. York, “Fish-oil dietary supplementation in patients with
Raynaud’s phenomenon: a double-blind, controlled, prospective study.,” 1989.
[4] D. R. Swanson, “Swanson 1988 Migraine and magnesium- Eleven neglected connections.pdf,” Perspect. Biol. Med., vol. 31,
no. 4, pp. 526–557, 1988.
[5] N. R. Smalheiser and D. R. Swanson, “Using ARROWSMITH: A computer-assisted approach to formulating and assessing
scientific hypotheses,” Comput. Methods Programs Biomed., vol. 57, no. 3, pp. 149–153, 1998.
[6] N. R. Smalheiser and D. R. Swanson, “Assessing a gap in the biomedical literature: Magnesium deficiency and neurologic
disease,” Neurosci. Res. Commun., vol. 15, no. 1, pp. 1–9, 1994.
[7] N. R. Smalheiser and D. R. Swanson, “Indomethacin and Alzheimer’s disease,” Neurol. , vol. 46 , no. 2 , p. 583, Feb. 1996.
[8] N. R. Smalheiser and D. R. Swanson, “Linking estrogen to Alzheimer ’ s disease : An informatics approach Hippocampal
formation size predicts declining memory performance in normal aging,” Neurology, vol. 47, pp. 809–810, 1996.
[9] N. R. Smalheiser and D. R. Swanson, “Calcium-independent phospholipase a2 and schizophrenia,” Arch. Gen. Psychiatry,
vol. 55, no. 8, pp. 752–753, 1998.
References
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[11] a Holzinger, R. Geierhofer, F. Mödritscher, and R. Tatzl, “Semantic Information in Medical Information Systems: Utilization
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[12] R. Mack, S. Mukherjea, a. Soffer, N. Uramoto, E. Brown, a. Coden, J. Cooper, a. Inokuchi, B. Iyer, Y. Mass, H. Matsuzawa,
and L. V. Subramaniam, “Text analytics for life science using the Unstructured Information Management Architecture,” IBM
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[13] X. Hu, “Mining novel connections from large online digital library using biomedical ontologies,” Libr. Manag., vol. 26, no.
4/5, pp. 261–270, 2005.
[14] P. Srinivasan, “MeSHmap: a text mining tool for MEDLINE.,” Proc. AMIA Symp., pp. 642–646, 2001.
[15] J. Demaine, J. Martin, B. De Bruijn, and B. De Bruijn, “Haystacks and Hypotheses,” in Proceedings of the ASIST Annual
Meeting, 2003, vol. 40, pp. 59–64.
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413, 2004.
[17] J. R. Katukuri, Y. Xie, V. V Raghavan, and A. Gupta, “Hypotheses generation as supervised link discovery with automated
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References
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[19] J. M. Vicente-Gomila, “The contribution of syntactic-semantic approach to the search for complementary literatures for
scientific or technical discovery,” Scientometrics, vol. 100, no. 3, pp. 659–673, 2014.
[20] M. Weeber, H. Klein, a R. Aronson, J. G. Mork, L. T. de Jong-van den Berg, and R. Vos, “Text-based discovery in
biomedicine: the architecture of the DAD-system.,” Proc. AMIA Symp., pp. 903–907, 2000.
[21] M. Weeber, H. Klein, L. T. W. De Jong-Van Den Berg, R. Vos, and L. T. W. D. J. Den Berg, “Using Concepts in LiteratureBased Discovery : Simulating Swanson ’ s Raynaud – Fish Oil and Migraine – Magnesium Discoveries,” J. Am. Soc. Inf. Sci.
Technol., vol. 52, no. 7, pp. 548–557, 2001.
[22] K. M. Hettne, M. Weeber, M. L. Laine, H. Ten Cate, S. Boyer, J. a. Kors, and B. G. Loos, “Automatic mining of the literature
to generate new hypotheses for the possible link between periodontitis and atherosclerosis: Lipopolysaccharide as a case
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[23] M. D. Gordon, M. D. Gordon, S. Dumais, and S. Dumais, “Using latent semantic indexing for literature-based discovery,” J.
Am. Soc. Inf. Sci. Technol., vol. 49, pp. 674–685, 1998.
[24] W. D. Maciel, A. C. Faria-Campos, M. a Gonçalves, and S. V. Campos, “Can the vector space model be used to identify
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[25] I. N. Sarkar, “A vector space model approach to identify genetically related diseases,” J. Am. Med. Informatics Assoc., vol.
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References
[26] S. Lee, J. Choi, K. Park, M. Song, and D. Lee, “Discovering context-specific relationships from biological literature by using
multi-level context terms,” BMC Med. Inform. Decis. Mak., vol. 12, no. Suppl 1, p. S1, 2012.
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[28] R. K. Lindsay, “Literature-based discovery by lexical statistics,” J. Am. Soc. Inf. Sci., vol. 50, no. 7, pp. 574–587, 1999.
[29] J. D. Wren, “Using fuzzy set theory and scale-free network properties to relate MEDLINE terms,” Soft Comput., vol. 10, no.
4, pp. 374–381, 2006.
[30] J. D. Wren, R. Bekeredjian, J. a. Stewart, R. V. Shohet, and H. R. Garner, “Knowledge discovery by automated
identification and ranking of implicit relationships,” Bioinformatics, vol. 20, no. 3, pp. 389–398, 2004.
[31] J. Atkinson and A. Rivas, “Discovering novel causal patterns from biomedical natural-language texts using Bayesian nets,”
IEEE Trans. Inf. Technol. Biomed., vol. 12, no. 6, pp. 714–722, 2008.
[32] T. Miyanishi, K. Seki, and K. Uehara, “Hypothesis generation and ranking based on event similarities,” Proc. 2010 ACM
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[33] V. I. Torvik and N. R. Smalheiser, “A quantitative model for linking two disparate sets of articles in MEDLINE,”
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[34] Y. Tsuruoka, M. Miwa, K. Hamamoto, J. Tsujii, and S. Ananiadou, “Discovering and visualizing indirect associations
between biomedical concepts,” Bioinformatics, vol. 27, no. 13, pp. 111–119, 2011.
References
[35] S. Spangler and A. Wilkins, “Automated hypothesis generation based on mining scientific literature,” in Proceedings of the
20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014, pp. 1877–1886.
[36] M. S. Hossain, J. Gresock, Y. Edmonds, R. Helm, M. Potts, and N. Ramakrishnan, “Connecting the dots between PubMed
abstracts,” PLoS One, vol. 7, no. 1, 2012.
[37] M. Weeber, “Drug discovery as an example of literature-based discovery,” Comput. Discov. Sci. Knowl., pp. 290–306,
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[38] R. Frijters, M. van Vugt, R. Smeets, R. van Schaik, J. de Vlieg, and W. Alkema, “Literature mining for the discovery of
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[39] D. Hristovski, B. Peterlin, J. a. Mitchell, and S. M. Humphrey, “Using literature-based discovery to identify disease
candidate genes,” Int. J. Med. Inform., vol. 74, no. 2–4, pp. 289–298, 2005.
[40] D. Hristovski, B. Peterlin, and S. Dzeroski, “Literature-based Discovery Support System and Its Application to Disease
Gene Identification,” Proc. AMIA Symp., p. 928, 2001.
[41] J. S. Wu, E. F. Kao, and C. N. Lee, “Discovering hidden connections among diseases, genes and drugs based on
microarray expression profiles with negative-term filtering,” PLoS One, vol. 9, no. 6, 2014.
[42] R. N. Kostoff, J. L. Solka, R. L. Rushenberg, and J. a. Wyatt, “Literature-related discovery (LRD): Water purification,”
Technol. Forecast. Soc. Change, vol. 75, no. 2, pp. 256–275, 2008.
[43] V. Ittipanuvat, K. Fujita, I. Sakata, and Y. Kajikawa, “Finding linkage between technology and social issue: A Literature
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