The DOI matches its Crossref record; 17 of the 18 references it cites were found in a registry or an index.
MedScan, a natural language processing engine for MEDLINE abstracts
Open accessAbstract
MOTIVATION: The importance of extracting biomedical information from scientific publications is well recognized. A number of information extraction systems for the biomedical domain have been reported, but none of them have become widely used in practical applications. Most proposals to date make rather simplistic assumptions about the syntactic aspect of natural language. There is an urgent need for a system that has broad coverage and performs well in real-text applications. RESULTS: We present a general biomedical domain-oriented NLP engine called MedScan that efficiently processes sentences from MEDLINE abstracts and produces a set of regularized logical structures representing the meaning of each sentence. The engine utilizes a specially developed context-free grammar and lexicon. Preliminary evaluation of the system's performance, accuracy, and coverage exhibited encouraging results. Further approaches for increasing the coverage and reducing parsing ambiguity of the engine, as well as its application for information extraction are discussed.
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Citations
Cites
Natural Language Understanding
(2021) · The MIT Press eBooks · doi:10.7551/mitpress/12761.003.0007 · Cited by 1,271
A General Natural-language Text Processor for Clinical Radiology
Friedman, C., Alderson, P. O., Austin, J. H. M., Cimino, J. J., & Johnson, S. B. (1994) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.1994.95236146 · Cited by 775
Lexical-Functional Grammar: A Formal System for Grammatical Representation
Kaplan, R. M., & Bresnan, J. (1995) · Cited by 725
Friedman, C., Kra, P., Yu, H., Krauthammer, M. O., & Rzhetsky, A. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.suppl_1.s74 · Cited by 508
Automatic extraction of biological information from scientific text: protein-protein interactions.
Blaschke, C., Andrade-Navarro, M. A., Ouzounis, C. A., & Valencia, A. (1999) · PubMed · Cited by 448
Automated extraction of information on protein–protein interactions from the biological literature
Ono, T., Hishigaki, H., Tanigami, A., & Takagi, T. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.2.155 · Cited by 398
Lectures on contemporary syntactic theories
Sells, P. (1985) · Cited by 260
Automatic Extraction of Protein Interactions from Scientific Abstracts
Thomas, J., Milward, D., Ouzounis, C. A., Pulman, S., & Carroll, M. (1999) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789814447331_0051 · Cited by 250
EVENT EXTRACTION FROM BIOMEDICAL PAPERS USING A FULL PARSER
Yakushiji, A., Tateisi, Y., Miyao, Y., & Tsujii, J. (2000) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789814447362_0040 · Cited by 226
Sekimizu, T., Park, & Tsujii, J. (1998) · PubMed · Cited by 196
Flagged first, then by citations
1 of the 18 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Humprhreys,K., Gaizauskas,R., Azzam,S., Huyck,C., Mitchell,B., Cuningham,H. and Wilks,Y. (1998) Description of the LaSIE-II System as used for MUC-7. Proceedings of the Seventh Message Understanding Conference.
Cited by
Sorted by citations
Items 201–236 of 236
Santos, C. F. (2008) · Deep Blue (University of Michigan)
Czarnecki, J. (2015) · Birkbeck Institutional Research Online (BiRO) - Theses · doi:10.18743/pub.00040135
Systems Pharmacology, Biomarkers, and Biomolecular Networks
Adourian, A. S., Plasterer, T. N., Balasubramanian, R., Jennings, E. G., Wang, S., Martin, S. A., van der Greef, J., McBurney, R. N., Muntendam, P., & Afeyan, N. B. (2008) · doi:10.1002/9780470431818.ch4
Ling, M. H., Lefèvre, C. M., & Nicholas, K. R. (2008) · arXiv (Cornell University) · doi:10.48550/arxiv.0804.0317
Protein–Protein Relation Extraction From Biomedical Abstracts
Ahmed, S. T., Davulcu, H., Tikves, S., Nair, R., & Patel, C. O. (2013) · doi:10.1002/9781118617151.ch41
GENDER DIFFERENCES IN CHEMICAL CARCINOGENESIS
Kadekar, S. (2013) · Figshare
Guner, U., Lee, J. H., Francone, O. L., & Leyfer, D. (2010) · IFAC Proceedings Volumes · doi:10.3182/20100705-3-be-2011.00016
Combining information extraction and text mining for cancer biomarker detection
Dawoud, K., Gao, S., Qabaja, A., Karampelas, P., & Alhajj, R. (2013) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/2492517.2500281
Bacha, J., Brodie, J., & Loose, M. (2009) · Mechanisms of Development · doi:10.1016/j.mod.2009.06.780
In silico discovery of on- and off-target effects of beta-blockers
Yuryev, A., & Griffies, M. J. (2010) · Toxicology · doi:10.1016/j.tox.2010.08.136
Li, C. X., Chen, P., Wang, R. J., Wang, X. J., Su, Y. R., & Li, J. (2014) · International Journal of Data Mining and Bioinformatics · doi:10.1504/ijdmb.2014.062890
Challenges in biological literature mining for online discovery of molecular interaction pathways
Ng, S.-K., & Tan, S. H. (2006) · International Journal of Computer Applications in Technology · doi:10.1504/ijcat.2006.011997
Biomarker Discovery with Text Mining and Literature Based Discovery
Zhu, F., & Shen, B. (2013) · Translational bioinformatics · doi:10.1007/978-94-007-7975-4_4
Chae, J.-M., Oh, H., Choi, S., Cha, C., Kim, M.-H., & Jung, S.-Y. (2008) · Annals of Laboratory Medicine · doi:10.3343/kjlm.2008.28.1.79
HaDextract: Extracting HLA-Disease Interaction Information from Biomedical Literature
Chae, J., Park, K. N., Jung, Y., Jung, S., Chae, J., & Oh, H. (2008) · International Conference on Future Generation Communication and Networking · doi:10.1109/fgcn.2008.161
Zhou, D., He, Y., & Kwoh, C. K. (2007) · IEEE Symposium on Computational Intelligence and Data Mining (CIDM) · doi:10.1109/cidm.2007.368941
Zhang, M. (2012) · OhioLink ETD Center (Ohio Library and Information Network)
Linking with BIAM: searching for drugs and pharmaceutical substances
Węgrzyn-Wolska, K. M. (2007)
In silicoanalysis strategies and resources for psychiatric genetics research
Torkamani, A., Bakken, T. E., & Schork, N. J. (2012) · Cambridge University Press eBooks · doi:10.1017/cbo9781139025997.005
Classification of Disease-Treatment HML
Prasadu, R. (2014) · International Journal of IT-based Public Health Management · doi:10.21742/ijiphm.2014.1.1.02
Nakaya, J., Shimizu, T., & Tanaka, H. (2006)
Relation extraction from biomedical text
Shi, Z. (2007) · Summit (Simon Fraser University)
A passiva possessiva em português: uma abordagem lexicalista com uma implementação computacional
de Alencar, L. F. (2018) · Fórum Linguístico · doi:10.5007/1984-8412.2018v15n4p3333
Clark, K. J. (2019) · RIT Scholar Works (Rochester Institute of Technology)
Prieto, M. (2019) · UPM Digital Archive (Technical University of Madrid) · doi:10.20868/upm.thesis.57930
Monogenetic rare diseases in biomedical databases and text miningPreprint
Nesterova, A. P., Климов, Е. А., Sozin, S., Sobolev, V. V., Linsley, P. S., & Golovatenko-Abramov, P. K. (2022) · medRxiv · doi:10.1101/2022.04.07.22273575
Inflammation as a Silent Partner in Opioid Addiction: A Regulatory Logic ModelPreprint
Toole, J. T., Morris, M. C., Lyman, C. A., Spink, G. L., & Broderick, G. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.26.489572
(2019) · Elsevier eBooks · doi:10.1016/b978-0-12-817086-1.09989-9
AUTOMATICALLY CLASSIFYING PUBMED ABSTRACTS AS BENCH OR BEDSIDE
(2017) · Issues in Information Systems · doi:10.48009/1_iis_2017_22-30
朱, 云., 李, 栋., 刘, 培., 李, 满., & 刘, 齐. (2010) · Scientia Sinica Vitae · doi:10.1360/zc2010-40-9-805
Escobar, J. M., & Higuita, D. S. T. (2026) · Quality & Quantity · doi:10.1007/s11135-026-02811-0
From prior knowledge to data-informed models: a review of Boolean network inference
Klemmer, P., Hemedan, A. A., Schneider, R., & Ostaszewski, M. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag499 · Cited by 1
Combining heterogeneous sources of data for the reverse-engineering of gene regulatory networks
Steele, E. (2010) · Brunel University Research Archive (BURA) (Brunel University London)
Combining text structure and meaning to support text mining
McDonald, D. M. (2006) · UA Campus Repository (The University of Arizona)
Metadata
- Authors
- Novichkova, S., Egorov, S., & Daraselia, N.
- Affiliations
- Ariadne Diagnostics (United States) (US)
- Corresponding author
- Novichkova, Svetlana
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2003-09-01
- Volume
- 19
- Issue
- 13
- Pages
- 1699-1706
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2140601463
- Cited by
- 236
- References
- 18
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 5 · 2023: 4 · 2022: 13 · 2021: 6 · 2020: 7 · 2019: 10 · 2018: 6 · 2017: 5 · 2016: 11 · 2015: 7 · 2014: 14 · 2013: 12 · 2012: 12
- FWCI
- 5.30
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Keywords
- MEDLINE abstracts, biomedical information extraction, natural language processing, context-free grammars, vocabulary, logical structure
- MeSH terms
- Abstracting and Indexing, Artificial Intelligence, Database Management Systems, Natural Language Processing, Pattern Recognition, Automated, Periodicals as Topic, Subject Headings, MEDLINE, Information Storage and Retrieval, Vocabulary, Controlled
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