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Cited by 76Biomedical Text Mining and OntologiesBioinformatics and Genomic NetworksComputational Drug Discovery Methods

A survey on literature based discovery approaches in biomedical domain

Open access
Gopalakrishnan, Vishrawas, Jha, Kishlay, Jin, Wei, Zhang, Aidong·2019·Journal of Biomedical Informatics
DOI 10.1016/j.jbi.2019.103141
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Abstract

Literature Based Discovery (LBD) refers to the problem of inferring new and interesting knowledge by logically connecting independent fragments of information units through explicit or implicit means. This area of research, which incorporates techniques from Natural Language Processing (NLP), Information Retrieval and Artificial Intelligence, has significant potential to reduce discovery time in biomedical research fields. Formally introduced in 1986, LBD has grown to be a significant and a core task for text mining practitioners in the biomedical domain. Together with its inter-disciplinary nature, this has led researchers across domains to contribute in advancing this field of study. This survey attempts to consolidate and present the evolution of techniques in this area. We cover a variety of techniques and provide a detailed description of the problem setting, the intuition, the advantages and limitations of various influential papers. We also list the current bottlenecks in this field and provide a general direction of research activities for the future. In an effort to be comprehensive and for ease of reference for off-the-shelf users, we also list many publicly available tools for LBD. We hope this survey will act as a guide to both academic and industry (bio)-informaticians, introduce the various methodologies currently employed and also the challenges yet to be tackled.

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  • SKiM-GPT's three-stage workflow discards many spurious associations.

    “This three-stage workflow discards many of the spurious associations as documented in earlier LBD studies [13]”

    From SKiM-GPT: combining biomedical literature-based discovery with large language model hypothesis evaluation (2025) · cites this paper as [13]

    Section 4.2 says semantic-relation approaches reduce false positives, and the Bio-SbKDS evaluation reports fewer, more relevant connections. These findings support the general rationale for filtering spurious LBD associations, but the survey does not evaluate SKiM-GPT or establish that its particular three-stage workflow discards many of them. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 39

  1. M. Girvan, M.E. Newman, Community structure in social and biological networks, Proc. Natl. Acad. Sci. USA 99 (12) (2002) 7821-7826.
  2. J. Ramos, Using TF-IDF to determine word relevance in document queries, in: Proceedings of the First Instructional Conference on Machine Learning, 2003.
  3. Y. Cao, J. Xu, T.-Y. Liu, H. Li, Y. Huang, H.-W. Hon, Adapting ranking svm to document retrieval, in: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2006, pp. 186-93.
  4. X. Wei, W.B. Croft, Lda-based document models for ad-hoc retrieval, in: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2006, pp. 178-85.
  5. P. Bruza, M. Weeber, Literature-Based Discovery, Springer, Science & Business Media, 2008.
  6. Y. Sebastian, E.-G. Siew, S.O. Orimaye, Emerging approaches in literature-based discovery: techniques and performance review, Knowl. Eng. Rev., vol. 32.
  7. W. Pratt, M. Yetisgen-Yildiz, Litlinker: capturing connections across the biome- dical literature, in: Proceedings of the 2nd International Conference on Knowledge Capture, 2003, pp. 105-12.
  8. D. Hristovski, C. Friedman, T.C. Rindflesch, B. Peterlin, Exploiting semantic re- lations for literature-based discovery, AMIA Annu. Symp. Proc. (2006) 349-353.
  9. V. Gopalakrishnan, K. Jha, A. Zhang, W. Jin, Generating hypothesis: Using global and local features in graph to discover new knowledge from medical literature, in: Proceedings of the 8th International Conference on Bioinformatics and Computational Biology, BICOB 2016, 2016, pp. 23-30.
  10. C.B. Ahlers, D. Hristovski, H. Kilicoglu, T.C. Rindflesch, Using the literature-based discovery paradigm to investigate drug mechanisms, AMIA Annu. Symp. Proc. (2007) 6-10.
  11. K. Jha, W. Jin, Mining novel knowledge from biomedical literature using statistical measures and domain knowledge, in: Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, 2016, pp. 317-26.
  12. A.R. Aronson, Effective mapping of biomedical text to the umls metathesaurus: the metamap program., in: Proceedings of the AMIA Symposium, 2001, p. 17.
  13. T.C. Rindflesch, A.R. Aronson, Ambiguity resolution while mapping free text to the UMLS Metathesaurus, Proc. Annu. Symp. Comput. Appl. Med. Care (1994) 240-244.
  14. A.R. Aronson, T.C. Rindflesch, Query expansion using the UMLS Metathesaurus, Proc. AMIA Annu. Fall Symp. (1997) 485-489.
  15. A.R. Aronson, The effect of textual variation on concept based information re- trieval, Proc. AMIA Annu. Fall Symp. (1996) 373-377.
  16. D. Hristovski, S. Džeroski, B. Peterlin, A. Rožić, Supporting discovery in medicine by association rule mining of bibliographic databases, European Conference on Principles of Data Mining and Knowledge Discovery, Springer, 2000, pp. 446-451.
  17. X. Hu, X. Zhang, I. Yoo, Y. Zhang, A semantic approach for mining hidden links from complementary and non-interactive biomedical literature., in: SDM, 2006, pp. 200-09.
  18. G. Li, X. Zhang, Mining biomedical knowledge using mutual information ABC, in: Granular Computing (GrC), 2011 IEEE International Conference on, 2011, pp. 848-50.
  19. G. Li, X. Zhang, Mining biomedical knowledge using Chi-square association rule, in: Granular Computing (GrC), in: 2010 IEEE International Conference on, 2010, pp. 283-85.
  20. I. Petrič, B. Cestnik, N. Lavrač, T. Urbančič, Bisociative knowledge discovery by literature outlier detection, Bisociative Knowledge Discovery, Springer, 2012, pp. 313-324.
  21. B. Sluban, M. Juršič, B. Cestnik, N. Lavrač, Exploring the power of outliers for cross-domain literature mining, Bisociative Knowledge Discovery, Springer, 2012, pp. 325-337.
  22. R. Zhang, T.J. Adam, G. Simon, M.J. Cairelli, T. Rindflesch, S. Pakhomov, G.B. Melton, Mining Biomedical literature to explore interactions between cancer drugs and dietary supplements, AMIA Jt Summits Transl. Sci. Proc. 2015 (2015) 69-73.
  23. B. Wilkowski, M. Fiszman, C.M. Miller, D. Hristovski, S. Arabandi, G. Rosemblat, T.C. Rindflesch, Graph-based methods for discovery browsing with semantic predications, AMIA Annu. Symp. Proc. 2011 (2011) 1514-23.
  24. M.J. Cairelli, C.M. Miller, M. Fiszman, T.E. Workman, T.C. Rindflesch, Semantic MEDLINE for discovery browsing: using semantic predications and the literature- based discovery paradigm to elucidate a mechanism for the obesity paradox, AMIA Annu. Symp. Proc. 2013 (2013) 164-73.
  25. H. Chalupsky, et al., Unsupervised link discovery in multi-relational data via rarity analysis, Third IEEE International Conference on Data Mining, 2003. ICDM 2003, IEEE, 2003, pp. 171-178.
  26. J.C. Goodwin, T. Cohen, T. Rindflesch, Discovery by scent: Discovery browsing system based on the information foraging theory, in: Bioinformatics and Biomedicine Workshops (BIBMW), 2012 IEEE International Conference on, 2012, pp. 232-39.
  27. J. Van Den Berg, M.J. Schuemie, Information retrieval systems using an associa- tive conceptual space., in: ESANN, 1999, pp. 351-56.
  28. N.R. Smalheiser, The arrowsmith project: 2005 status report, in: International Conference on Discovery Science, 2005, pp. 26-43.
  29. D. Hristovski, A. Kastrin, B. Peterlin, T.C. Rindflesch, Combining Semantic Relations and DNA Microarray Data for Novel Hypotheses Generation, Linking Literature, Information, and Knowledge for Biology, Springer, 2010, pp. 53-61.
  30. I. Pohl, Bi-directional and heuristic search in path problems, PhD thesis, Dept. of Computer Science, Stanford University, 1969.
  31. L. Yao, A. Haghighi, S. Riedel, A. McCallum, Structured relation discovery using generative models, in: Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2011, pp. 1456-66.
  32. J. Mower, D. Subramanian, N. Shang, T. Cohen, Classification-by-analogy: using vector representations of implicit relationships to identify plausibly causal drug/ side-effect relationships, in: AMIA Annual Symposium Proceedings, Vol. 2016, American Medical Informatics Association, 2016, pp. 1940.
  33. T. Mikolov, I. Sutskever, K. Chen, G.S. Corrado, J. Dean, Distributed representa- tions of words and phrases and their compositionality, in: Advances in Neural Information Processing Systems, 2013, pp. 3111-3119.
  34. T. Cohen, D. Widdows, L. De Vine, R. Schvaneveldt, T.C. Rindflesch, Many paths lead to discovery: analogical retrieval of cancer therapies, International Symposium on Quantum Interaction, Springer, 2012, pp. 90-101.
  35. G. Xun, K. Jha, V. Gopalakrishnan, Y. Li, A. Zhang, Generating medical hypotheses based on evolutionary medical concepts, Data Mining (ICDM), 2017 IEEE International Conference on, IEEE, 2017, pp. 535-544.
  36. K. Jha, Y. Wang, V. Gopalakrishnan, A. Zhang, et al., Concepts-bridges: Uncovering conceptual bridges based on biomedical concept evolution, in: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, ACM, 2018, pp. 1599-1607.
  37. A. Zhang, G. Xun, H.Q. Ngo, K. Jha, V. Gopalakrishnan, Towards self-learning based hypotheses generation in biomedical text domain, Bioinformatics 34 (12) (2017) 2103-2115, https://doi.org/10.1093/bioinformatics/btx837 arXiv:http:// oup.prod.sis.lan/bioinformatics/article-pdf/34/12/2103/25051548/btx837.pd. · DOI
  38. D. Cameron, R. Kavuluru, O. Bodenreider, P.N. Mendes, A.P. Sheth, K. Thirunarayan, Semantic predications for complex information needs in biomedical literature, in: Bioinformatics and Biomedicine (BIBM), 2011 IEEE International Conference on, 2011, pp. 512-19.
  39. Z. Lu, PubMed and beyond: a survey of web tools for searching biomedical lit- erature, Database (Oxford) 2011 (2011) baq036.

Cited by

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Authors
Gopalakrishnan, V., Jha, K., Jin, W., & Zhang, A.
Affiliations
  • University at Buffalo, State University of New York (US)
  • University of Virginia (US)
  • University of North Texas (US)
Corresponding author
  • Gopalakrishnan, Vishrawas
  • Jha, Kishlay
ORCID
Venue
Journal of Biomedical Informatics
Publisher
Elsevier BV
Published
2019-05
Volume
93
Pages
103141
ISSN
1532-0464 (ISSN-L)
DOI
10.1016/j.jbi.2019.103141
OpenAlex
W2921377602
Cited by
76
References
2
Citations by year
2026: 4 · 2025: 8 · 2024: 10 · 2023: 14 · 2022: 11 · 2021: 10 · 2020: 11 · 2019: 8
FWCI
0.88
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Biomedical Informatics (publishedVersion)
  • PubMed (publishedVersion)
Funders
US National Science Foundation (NSF IIS-1218393, IIS-1514204)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Keywords
literature-based discovery, biomedical text mining, information integration
MeSH terms
Humans, Natural Language Processing, Surveys and Questionnaires, Data Mining, Knowledge Discovery

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