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Cited by 65Biomedical Text Mining and OntologiesBioinformatics and Genomic NetworksAdvanced Text Analysis Techniques

LION LBD: a literature-based discovery system for cancer biology

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Pyysalo, Sampo, Baker, Simon, Ali, Imran, Haselwimmer, Stefan, Shah, Tejas, Young, Andrew, Guo, Yufan, Högberg, Johan +3 more·2019·Bioinformatics
DOI 10.1093/bioinformatics/bty845PMC PMC6499247
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Abstract

Motivation: The overwhelming size and rapid growth of the biomedical literature make it impossible for scientists to read all studies related to their work, potentially leading to missed connections and wasted time and resources. Literature-based discovery (LBD) aims to alleviate these issues by identifying implicit links between disjoint parts of the literature. While LBD has been studied in depth since its introduction three decades ago, there has been limited work making use of recent advances in biomedical text processing methods in LBD. Results: We present LION LBD, a literature-based discovery system that enables researchers to navigate published information and supports hypothesis generation and testing. The system is built with a particular focus on the molecular biology of cancer using state-of-the-art machine learning and natural language processing methods, including named entity recognition and grounding to domain ontologies covering a wide range of entity types and a novel approach to detecting references to the hallmarks of cancer in text. LION LBD implements a broad selection of co-occurrence based metrics for analyzing the strength of entity associations, and its design allows real-time search to discover indirect associations between entities in a database of tens of millions of publications while preserving the ability of users to explore each mention in its original context in the literature. Evaluations of the system demonstrate its ability to identify undiscovered links and rank relevant concepts highly among potential connections. Availability and implementation: The LION LBD system is available via a web-based user interface and a programmable API, and all components of the system are made available under open licenses from the project home page http://lbd.lionproject.net. Supplementary information: Supplementary data are available at Bioinformatics online.

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  • Existing LBD methods and tools primarily quantify the strength of association without assessing relevance to or support of a specific scientific hypothesis.

    “However, while existing LBD methods can efficiently identify statistically significant associations between biomedical terms, they generally lack a structured framework for evaluating whether these discovered associations meaningfully support or contradict a specific scientific hypothesis.”

    From SKiM-GPT: combining biomedical literature-based discovery with large language model hypothesis evaluation (2025) · cites this paper as [27]

    Section 3.3 describes LBD scores based on co-occurrence and statistical association, and Section 4.3 says the system can suggest unverifiable or causally invalid connections. These passages support the concern that association scores alone do not establish meaningful support for a hypothesis. The paper does not establish the broader claim that existing LBD methods generally lack a structured framework for assessing whether evidence supports or contradicts a specific hypothesis; its Introduction also describes closed discovery as hypothesis testing. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 40

  1. Aronson A.R. (2001) Effective mapping of biomedical text to the UMLS metathesaurus: the MetaMap program. In: Proceedings of the AMIA Symposium, p. 17 American Medical Informatics Association, Washington, DC. PMC2243666 11825149
  2. Baker S., Korhonen A. (2017) Initializing neural networks for hierarchical multi-label text classification. In: BioNLP, 2017, Association for Computational Linguistics, pp. 307–315.
  3. Baker S. et al (2016) Automatic semantic classification of scientific literature according to the hallmarks of cancer. Bioinformatics, 32, 432– 440. 26454282
  4. Baker S. et al (2017) Cancer Hallmarks Analytics Tool (CHAT): a text mining approach to organize and evaluate scientific literature on cancer. Bioinformatics, 33, 3973– 3981. 29036271 PMC5860084
  5. Bodenreider O. (2004) The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res., 32, D267– D270. 14681409 PMC308795
  6. Degtyarenko K. et al (2007) ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Res., 36, D344– D350. 17932057 PMC2238832
  7. DeNicola G.M. et al (2011) Oncogene-induced Nrf2 transcription promotes ROS detoxification and tumorigenesis. Nature, 475, 106– 109. 21734707 PMC3404470
  8. Doğan R.I. et al (2014) NCBI disease corpus: a resource for disease name recognition and concept normalization. J. Biomed. Inf., 47, 1– 10. PMC3951655 24393765
  9. Federhen S. (2012) The NCBI taxonomy database. Nucleic Acids Res., 40, D136– D143. 22139910 PMC3245000
  10. Fielding R.T., Taylor R.N. (2000) Architectural styles and the design of network-based software architectures, Vol. 7 Irvine Doctoral Dissertation, University of California.
  11. Gaffen S.L., McGeachy M. (2015) Integrating p38α-MAPK immune signals in non-immune cells. Sci. Signal., 8, fs5. 25737584 PMC4805373
  12. Hanahan D., Weinberg R.A. (2000) The hallmarks of cancer. Cell, 100, 57– 70. 10647931
  13. Hanahan D., Weinberg R.A. (2011) Hallmarks of cancer: the next generation. Cell, 144, 646– 674. 21376230
  14. Heijden V.D. et al (2016) Bcl-2 is a critical mediator of intestinal transformation. Nat. Commun., 7, 1– 11. PMC4786877 26956214
  15. Hristovski D. et al (2005) Using literature-based discovery to identify disease candidate genes. Int. J. Med. Inf., 74, 289– 298. 15694635
  16. Huang H. et al (2011) A comprehensive protein-centric ID mapping service for molecular data integration. Bioinformatics, 27, 1190– 1191. 21478197 PMC3072559
  17. Kim J.-D. et al (2004) Introduction to the bio-entity recognition task at JNLPBA. In: Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and Its Applications. Association for Computational Linguistics, pp. 70– 75.
  18. Krallinger M. et al (2015) CHEMDNER: the drugs and chemical names extraction challenge. J. Cheminf., 7, S1. PMC4331685 25810766
  19. Lipscomb C.E. (2000) Medical subject headings (MeSH). Bull. Med. Library Assoc., 88, 265. PMC35238 10928714
  20. Maglott D. et al (2004) Entrez Gene: gene-centered information at NCBI. Nucleic Acids Res., 33, D54– D58. PMC539985 15608257
  21. Morgan A.A. et al (2008) Overview of BioCreative II gene normalization. Genome Biol., 9, S3. PMC2559987 18834494
  22. Natale D.A. et al (2014) Protein Ontology: a controlled structured network of protein entities. Nucleic Acids Res., 42, D415– D421. 24270789 PMC3964965
  23. Preiss J. et al (2012) Towards semantic literature based discovery. In: 2012 AAAI Fall Symposium Series: Information Retrieval and Knowledge Discovery in Biomedical Text, Vol. 30, pp. 7– 18.
  24. Shannon P. et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res., 13, 2498– 2504. 14597658 PMC403769
  25. Simpson M.S., Demner-Fushman D. (2012) Biomedical text mining: a survey of recent progress. In: Mining Text Data. Springer, Boston, MA, pp. 465– 517.
  26. Smalheiser N.R., Swanson D.R. (1996a) Indomethacin and Alzheimer’s disease. Neurology, 46, 583. 8614542
  27. Smalheiser N.R., Swanson D.R. (1996b) Linking estrogen to Alzheimer’s disease an informatics approach. Neurology, 47, 809– 810. 8797484
  28. Smalheiser N.R., Swanson D.R. (1998) Calcium-independent phospholipase a2 and schizophrenia. Arch. Gen. Psychiatry, 55, 752– 753. 9707387
  29. Smith L. et al (2008) Overview of BioCreative II gene mention recognition. Genome Biol., 9, S2. PMC2559986 18834493
  30. Swanson D.R. (1986a) Fish oil, raynaud’s syndrome, and undiscovered public knowledge. Perspect. Biol. Med., 30, 7– 18. 3797213
  31. Swanson D.R. (1988) Migraine and magnesium: eleven neglected connections. Perspect. Biol. Med., 31, 526– 557. 3075738
  32. Swanson D.R. (1990) Somatomedin c and arginine: implicit connections between mutually isolated literatures. Perspect. Biol. Med., 33, 157– 186. 2406696
  33. Tsuruoka Y. et al (2008) FACTA: a text search engine for finding associated biomedical concepts. Bioinformatics, 24, 2559– 2560. 18772154 PMC2572701
  34. Venkatraman G. et al (2015) Lysophosphatidate signaling stabilizes nrf2 and increases the expression of genes involved in drug resistance and oxidative stress responses: implications for cancer treatment. FASEB J., 29, 772– 785. 25398768
  35. Weeber M. et al (2005) Online tools to support literature-based discovery in the life sciences. Brief. Bioinf., 6, 277– 286. 16212775
  36. Wei C.-H. et al (2013) PubTator: a web-based text mining tool for assisting biocuration. Nucleic Acids Res., 41, W518– W522. 23703206 PMC3692066
  37. Wei C.-H. et al (2016) Assessing the state of the art in biomedical relation extraction: overview of the BioCreative V chemical-disease relation (CDR) task. Database, 2016, baw032. 26994911 PMC4799720
  38. Wheeler D.L. et al (2003) Database resources of the national center for biotechnology. Nucleic Acids Res., 31, 28– 33. 12519941 PMC165480
  39. Yetisgen-Yildiz M., Pratt W. (2008) Evaluation of literature-based discovery systems. In: Literature-Based Discovery. Springer, pp. 101– 113. 19124086
  40. Yetisgen-Yildiz M., Pratt W. (2009) A new evaluation methodology for literature-based discovery systems. J. Biomed. Inf., 42, 633– 643. 19124086

Cited by

Sorted by citations

Authors
Pyysalo, S., Baker, S., Ali, I., Haselwimmer, S., Shah, T., Young, A., Guo, Y., Högberg, J., Stenius, U., Narita, M., & Korhonen, A.
Affiliations
  • University of Cambridge (GB)
  • Karolinska Institutet (SE)
Corresponding author
Pyysalo, Sampo
ORCID
Venue
Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2018-10-09
Volume
35
Issue
9
Pages
1553-1561
ISSN
  • 1367-4803 (ISSN-L)
  • 1367-4811
DOI
10.1093/bioinformatics/bty845
PMC
PMC6499247
OpenAlex
W2896730986
Cited by
65
References
2
Citations by year
2026: 1 · 2025: 8 · 2024: 10 · 2023: 11 · 2022: 7 · 2021: 7 · 2020: 10 · 2019: 11
FWCI
2.49
Type
journal article
Language
en
Open access
Licence
Locations
  • Bioinformatics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Apollo (University of Cambridge) (submittedVersion, cc-by)
  • Apollo (cc-by)
Funders
  • Research Councils UK (MR/M013049/1)
  • Cancer Research UK Cambridge Institute Core (C14303/A17197)
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 › Artificial Intelligence › Advanced Text Analysis Techniques
Keywords
literature-based discovery, tumor biology, biomedical text mining, named entity recognition, ontology grounding, cancer hallmarks, entity association, hypothesis generation
MeSH terms
Algorithms, Humans, Natural Language Processing, Neoplasms, Publications, Databases, Factual

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