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Biomedical text mining

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Biomedical text mining (including biomedical natural language processing or BioNLP) refers to the methods and study of how text mining may be applied to texts and literature of the biomedical and molecular biology domains. As a field of research, biomedical text mining incorporates ideas from natural language processing, bioinformatics, medical informatics and computational linguistics. The strategies developed through studies in this field are frequently applied to the biomedical and molecular biology literature available through services such as PubMed.

Considerations

Applying text mining approaches to biomedical text requires specific considerations common to the domain.

Availability of annotated text data

This figure presents several properties of a biomedical literature corpus prepared by Westergaard et al.[1] The corpus includes 15 million English-language full text articles.(a) Number of publications per year from 1823–2016. (b) Temporal development in the distribution of six different topical categories from 1823–2016. (c) Development in the number of pages per article from 1823–2016.

Large annotated corpora used in the development and training of general purpose text mining methods (e.g., sets of movie dialogue,[2] product reviews,[3] or Wikipedia article text) are not specific for biomedical language. While they may provide evidence of general text properties such as parts of speech, they rarely contain concepts of interest to biologists or clinicians. Development of new methods to identify features specific to biomedical documents therefore requires assembly of specialized corpora.[4] Resources designed to aid in building new biomedical text mining methods have been developed through the Informatics for Integrating Biology and the Bedside (i2b2) challenges[5][6][7] and biomedical informatics researchers.[8][9] Text mining researchers frequently combine these corpora with the controlled vocabularies and ontologies available through the National Library of Medicine's Unified Medical Language System (UMLS) and Medical Subject Headings (MeSH).

Machine learning-based methods often require very large data sets as training data to build useful models.[10] Manual annotation of large text corpora is not realistically possible. Training data may therefore be products of weak supervision[11][12] or purely statistical methods.

Data structure variation

Like other text documents, biomedical documents contain unstructured data.[13] Research publications follow different formats, contain different types of information, and are interspersed with figures, tables, and other non-text content. Both unstructured text and semi-structured document elements, such as tables, may contain important information that should be text mined.[14] Clinical documents may vary in structure and language between departments and locations. Other types of biomedical text, such as drug labels,[15] may follow general structural guidelines but lack further details.

Uncertainty

Biomedical literature contains statements about observations that may not be statements of fact. This text may express uncertainty or skepticism about claims. Without specific adaptations, text mining approaches designed to identify claims within text may mis-characterize these "hedged" statements as facts.[16]

Supporting clinical needs

Biomedical text mining applications developed for clinical use should ideally reflect the needs and demands of clinicians.[4] This is a concern in environments where clinical decision support is expected to be informative and accurate.

Interoperability with clinical systems

New text mining systems must work with existing standards, electronic medical records, and databases.[4] Methods for interfacing with clinical systems such as LOINC have been developed[17] but require extensive organizational effort to implement and maintain.[18][19]

Patient privacy

Text mining systems operating with private medical data must respect its security and ensure it is rendered anonymous where appropriate.[20][21][22]

Processes

Specific sub tasks are of particular concern when processing biomedical text.[13]

Named entity recognition

Developments in biomedical text mining have incorporated identification of biological entities with named entity recognition, or NER. Names and identifiers for biomolecules such as proteins and genes,[23] chemical compounds and drugs,[24] and disease names[25] have all been used as entities. Most entity recognition methods are supported by pre-defined linguistic features or vocabularies, though methods incorporating deep learning and word embeddings have also been successful at biomedical NER.[26]

Document classification and clustering

Biomedical documents may be classified or clustered based on their contents and topics. In classification, document categories are specified manually,[27] while in clustering, documents form algorithm-dependent, distinct groups.[28] These two tasks are representative of supervised and unsupervised methods, respectively, yet the goal of both is to produce subsets of documents based on their distinguishing features. Methods for biomedical document clustering have relied upon k-means clustering.[28]

Relationship discovery

Biomedical documents describe connections between concepts, whether they are interactions between biomolecules, events occurring subsequently over time (i.e., temporal relationships), or causal relationships. Text mining methods may perform relation discovery to identify these connections, often in concert with named entity recognition.[29]

Hedge cue detection

The challenge of identifying uncertain or "hedged" statements has been addressed through hedge cue detection in biomedical literature.[16]

Claim detection

Multiple researchers have developed methods to identify specific scientific claims from literature.[30][31] In practice, this process involves both isolating phrases and sentences denoting the core arguments made by the authors of a document (a process known as argument mining, employing tools used in fields such as political science) and comparing claims to find potential contradictions between them.[31]

Information extraction

Information extraction, or IE, is the process of automatically identifying structured information from unstructured or partially structured text. IE processes can involve several or all of the above activities, including named entity recognition, relationship discovery, and document classification, with the overall goal of translating text to a more structured form, such as the contents of a template or knowledge base. In the biomedical domain, IE is used to generate links between concepts described in text, such as gene A inhibits gene B and gene C is involved in disease G.[32] Biomedical knowledge bases containing this type of information are generally products of extensive manual curation, so replacement of manual efforts with automated methods remains a compelling area of research.[33][34]

Information retrieval and question answering

Biomedical text mining supports applications for identifying documents and concepts matching search queries. Search engines such as PubMed search allow users to query literature databases with words or phrases present in document contents, metadata, or indices such as MeSH. Similar approaches may be used for medical literature retrieval. For more fine-grained results, some applications permit users to search with natural language queries and identify specific biomedical relationships.[35]

On 16 March 2020, the National Library of Medicine and others launched the COVID-19 Open Research Dataset (CORD-19) to enable text mining of the current literature on the novel virus. The dataset is hosted by the Semantic Scholar project[36] of the Allen Institute for AI.[37] Other participants include Google, Microsoft Research, the Center for Security and Emerging Technology, and the Chan Zuckerberg Initiative.[38]

Resources

Corpora

The following table lists a selection of biomedical text corpora and their contents. These items include annotated corpora, sources of biomedical research literature, and resources frequently used as vocabulary and/or ontology references, such as MeSH. Items marked "Yes" under "Freely Available" can be downloaded from a publicly accessible location.

Biomedical Text Corpora
Corpus Name Authors or Group Contents Freely Available Citation
2006 i2b2 Deidentification and Smoking Challenge i2b2 889 de-identified medical discharge summaries annotated for patient identification and smoking status features. Yes, with registration [39][40]
2008 i2b2 Obesity Challenge i2b2 1,237 de-identified medical discharge summaries annotated for presence or absence of comorbidities of obesity. Yes, with registration [41]
2009 i2b2 Medication Challenge i2b2 1,243 de-identified medical discharge summaries annotated for names and details of medications, including dosage, mode, frequency, duration, reason, and presence in a list or narrative structure. Yes, with registration [42][43]
2010 i2b2 Relations Challenge i2b2 Medical discharge summaries annotated for medical problems, tests, treatments, and the relations among these concepts. Only a subset of these data records are available for research use due to IRB limitations. Yes, with registration [5]
2011 i2b2 Coreference Challenge i2b2 978 de-identified medical discharge summaries, progress notes, and other clinical reports annotated with concepts and coreferences. Includes the ODIE corpus. Yes, with registration [44]
2012 i2b2 Temporal Relations Challenge i2b2 310 de-identified medical discharge summaries annotated for events and temporal relations. Yes, with registration [6]
2014 i2b2 De-identification Challenge i2b2 1,304 de-identified longitudinal medical records annotated for protected health information (PHI). Yes, with registration [45]
2014 i2b2 Heart Disease Risk Factors Challenge i2b2 1,304 de-identified longitudinal medical records annotated for risk factors for cardiac artery disease. Yes, with registration [46]
AIMed Bunescu et al. 200 abstracts annotated for protein–protein interactions, as well as negative example abstracts containing no protein-protein interactions. Yes [47]
BioC-BioGRID BioCreAtIvE 120 full text research articles annotated for protein–protein interactions. Yes [48]
BioCreAtIvE 1 BioCreAtIvE 15,000 sentences (10,000 training and 5,000 test) annotated for protein and gene names. 1,000 full text biomedical research articles annotated with protein names and Gene Ontology terms. Yes [49]
BioCreAtIvE 2 BioCreAtIvE 15,000 sentences (10,000 training and 5,000 test, different from the first corpus) annotated for protein and gene names. 542 abstracts linked to EntrezGene identifiers. A variety of research articles annotated for features of protein–protein interactions. Yes [50]
BioCreative V CDR Task Corpus (BC5CDR) BioCreAtIvE 1,500 articles (title and abstract) published in 2014 or later, annotated for 4,409 chemicals, 5,818 diseases and 3116 chemical–disease interactions. Yes [51]
BioInfer Pyysalo et al. 1,100 sentences from biomedical research abstracts annotated for relationships, named entities, and syntactic dependencies. No [52]
BioScope Vincze et al. 1,954 clinical reports, 9 papers, and 1,273 abstracts annotated for linguistic scope and terms denoting negation or uncertainty. Yes [53]
BioText Recognizing Abbreviation Definitions BioText Project 1,000 abstracts on the subject of "yeast", annotated for abbreviations and their meanings. Yes [54]
BioText Protein–Protein Interaction Data BioText Project 1,322 sentences describing protein–protein interactions between HIV-1 and human proteins, annotated with interaction types. Yes [55]
Comparative Toxicogenomics Database Davis et al. A database of manually-curated associations between chemicals, gene products, phenotypes, diseases, and environmental exposures. Yes [56]
CRAFT Verspoor et al. 97 full-text biomedical publications annotated with linguistic structures and biological concepts Yes [57]
GENIA Corpus GENIA Project 1,999 biomedical research abstracts on the topics "human", "blood cells", and "transcription factors", annotated for parts of speech, syntax, terms, events, relations, and coreferences. Yes [58][59]
FamPlex Bachman et al. Protein names and families linked to unique identifiers. Includes affix sets. Yes [60]
FlySlip Abstracts FlySlip 82 research abstracts on Drosophila annotated with gene names. Yes [61]
FlySlip Full Papers FlySlip 5 research papers on Drosophila annotated with anaphoric relations between noun phrases referring to genes and biologically related entities. Yes [62]
FlySlip Speculative Sentences FlySlip More than 1,500 sentences annotated as speculative or not speculative. Includes annotations of clauses. Yes [63]
IEPA Ding et al. 486 sentences from biomedical research abstracts annotated for pairs of co-occurring chemicals, including proteins. No [64]
JNLPBA corpus Kim et al. An extended version of version 3 of the GENIA corpus for NER tasks. No [65]
Learning Language in Logic (LLL) Nédellec et al. 77 sentences from research articles about the bacterium Bacillus subtilis, annotated for protein–gene interactions. Yes [66]
Medical Subject Headings (MeSH) National Library of Medicine Hierarchically-organized terminology for indexing and cataloging biomedical documents. Yes [67]
Metathesaurus National Library of Medicine / UMLS 3.67 million concepts and 14 million concept names, mapped between more than 200 sources of biomedical vocabulary and identifiers. Yes, with UMLS License Agreement [68][69]
MIMIC-III MIT Lab for Computational Physiology de-identified data associated with 53,423 distinct hospital admissions for adult patients. Requires training and formal access request [70]
ODIE Corpus Savova et al. 180 clinical notes annotated with 5,992 coreference pairs. No [71]
OHSUMED Hersh et al. 348,566 biomedical research abstracts and indexing information from MEDLINE, including MeSH (as of 1991). Yes [72]
PMC Open Access Subset National Library of Medicine / PubMed Central More than 2 million research articles, updated weekly. Yes [73]
RxNorm National Library of Medicine / UMLS Normalized names for clinical drugs and drug packs, with combined ingredients, strengths, and form, and assigned types from the Semantic Network. Yes, with UMLS License Agreement [74]
Semantic Network National Library of Medicine / UMLS Lists of 133 semantic types and 54 semantic relationships covering biomedical concepts and vocabulary. Yes, with UMLS License Agreement [75][76]
SPECIALIST Lexicon National Library of Medicine / UMLS A syntactic lexicon of biomedical and general English. Yes [77][78]
Word Sense Disambiguation (WSD) National Library of Medicine / UMLS 203 ambiguous words and 37,888 automatically extracted instances of their use in biomedical research publications. Yes, with UMLS License Agreement [79][80]
Yapex Franzén et al. 200 biomedical research abstracts annotated with protein names. No [81]

Word embeddings

Several groups have developed sets of biomedical vocabulary mapped to vectors of real numbers, known as word vectors or word embeddings. Sources of pre-trained embeddings specific for biomedical vocabulary are listed in the table below. The majority are results of the word2vec model developed by Mikolov et al[82] or variants of word2vec.

Biomedical word embeddings
Set Name Authors or Group Contents and Source Citation
BioASQword2vec BioASQ Vectors produced by word2vec from 10,876,004 English PubMed abstracts. [83]
bio.nlplab.org resources Pyysalo et al. A collection of word vectors produced by different approaches, trained on text from PubMed and PubMed Central. [84]
BioVec Asgari and Mofrad Vectors for gene and protein sequences, trained using Swiss-Prot. [85]
RadiologyReportEmbedding Banerjee et al. Vectors produced by word2vec from the text of 10,000 radiology reports. [86]

Applications

A flowchart of a text mining protocol.
An example of a text mining protocol used in a study of protein-protein complexes, or protein docking.[87]

Text mining applications in the biomedical field include computational approaches to assist with studies in protein docking,[87] protein interactions,[88][89] and protein-disease associations.[90]

Gene cluster identification

Methods for determining the association of gene clusters obtained by microarray experiments with the biological context provided by the corresponding literature have been developed.[91]

Protein interactions

Automatic extraction of protein interactions[92] and associations of proteins to functional concepts (e.g. gene ontology terms) has been explored.[citation needed] The search engine PIE was developed to identify and return protein-protein interaction mentions from MEDLINE-indexed articles.[93] The extraction of kinetic parameters from text or the subcellular location of proteins have also been addressed by information extraction and text mining technology.[citation needed]

Gene-disease associations

Text mining can aid in gene prioritization, or identification of genes most likely to contribute to genetic disease. One group compared several vocabularies, representations and ranking algorithms to develop gene prioritization benchmarks.[94]

Gene-trait associations

An agricultural genomics group identified genes related to bovine reproductive traits using text mining, among other approaches.[95]

Protein-disease associations

Text mining enables an unbiased evaluation of protein-disease relationships within a vast quantity of unstructured textual data.[96]

Applications of phrase mining to disease associations

A text mining study assembled a collection of 709 core extracellular matrix proteins and associated proteins based on two databases: MatrixDB (matrixdb.univ-lyon1.fr) and UniProt. This set of proteins had a manageable size and a rich body of associated information, making it a suitable for the application of text mining tools. The researchers conducted phrase-mining analysis to cross-examine individual extracellular matrix proteins across the biomedical literature concerned with six categories of cardiovascular diseases. They used a phrase-mining pipeline, Context-aware Semantic Online Analytical Processing (CaseOLAP),[97] then semantically scored all 709 proteins according to their Integrity, Popularity, and Distinctiveness using the CaseOLAP pipeline. The text mining study validated existing relationships and informed previously unrecognized biological processes in cardiovascular pathophysiology.[90]

Software tools

Search engines

Search engines designed to retrieve biomedical literature relevant to a user-provided query frequently rely upon text mining approaches. Publicly available tools specific for research literature include PubMed search, Europe PubMed Central search, GeneView,[98] and APSE[99] Similarly, search engines and indexing systems specific for biomedical data have been developed, including DataMed[100] and OmicsDI.[101]

Some search engines, such as Essie,[102] OncoSearch,[103] PubGene,[104][105] and GoPubMed[106] were previously public but have since been discontinued, rendered obsolete, or integrated into commercial products.

Medical record analysis systems

Electronic medical records (EMRs) and electronic health records (EHRs) are collected by clinical staff in the course of diagnosis and treatment. Though these records generally include structured components with predictable formats and data types, the remainder of the reports are often free-text. Numerous complete systems and tools have been developed to analyse these free-text portions.[107] The MedLEE system was originally developed for analysis of chest radiology reports but later extended to other report topics.[108] The clinical Text Analysis and Knowledge Extraction System, or cTAKES, annotates clinical text using a dictionary of concepts.[109] The CLAMP system offers similar functionality with a user-friendly interface.[110]

Frameworks

Computational frameworks have been developed to rapidly build tools for biomedical text mining tasks. SwellShark[111] is a framework for biomedical NER that requires no human-labeled data but does make use of resources for weak supervision (e.g., UMLS semantic types). The SparkText framework[112] uses Apache Spark data streaming, a NoSQL database, and basic machine learning methods to build predictive models from scientific articles.

APIs

Some biomedical text mining and natural language processing tools are available through application programming interfaces, or APIs. NOBLE Coder performs concept recognition through an API.[113]

Conferences

The following academic conferences and workshops host discussions and presentations in biomedical text mining advances. Most publish proceedings.

Conferences for Biomedical Text Mining
Conference Name Session Proceedings
Association for Computational Linguistics (ACL) annual meeting plenary session and as part of the BioNLP workshop
ACL BioNLP workshop [114]
American Medical Informatics Association (AMIA) annual meeting in plenary session
Intelligent Systems for Molecular Biology (ISMB) in plenary session and in the BioLINK and Bio-ontologies workshops [115]
International Conference on Bioinformatics and Biomedicine (BIBM) [116]
International Conference on Information and Knowledge Management (CIKM) within International Workshop on Data and Text Mining in Biomedical Informatics (DTMBIO) [117]
North American Association for Computational Linguistics (NAACL) annual meeting plenary session and as part of the BioNLP workshop
Pacific Symposium on Biocomputing (PSB) in plenary session [118]
Practical Applications of Computational Biology & Bioinformatics (PACBB) [119]
Text REtrieval Conference (TREC) formerly as part of TREC Genomics track; as of 2018 part of Precision Medicine Track [120]

Journals

See also: List of medical and health informatics journals

A variety of academic journals publishing manuscripts on biology and medicine include topics in text mining and natural language processing software. Some journals, including the Journal of the American Medical Informatics Association (JAMIA) and the Journal of Biomedical Informatics are popular publications for these topics.

References

  1. ^ Westergaard D, Stærfeldt HH, Tønsberg C, Jensen LJ, Brunak S (February 2018). "A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts". PLOS Computational Biology. 14 (2): e1005962. Bibcode:2018PLSCB..14E5962W. doi:10.1371/journal.pcbi.1005962. PMC 5831415. PMID 29447159.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  2. ^ Danescu-Niculescu-Mizil C, Lee L (2011). Chameleons in Imagined Conversations: A New Approach to Understanding Coordination of Linguistic Style in Dialogs. pp. 76–87. arXiv:1106.3077. Bibcode:2011arXiv1106.3077D. ISBN 978-1-932432-95-4. {{cite book}}: |journal= ignored (help)
  3. ^ McAuley J, Leskovec J (2013-10-12). Hidden factors and hidden topics: understanding rating dimensions with review text. ACM. pp. 165–172. doi:10.1145/2507157.2507163. ISBN 978-1-4503-2409-0. S2CID 6440341.
  4. ^ a b c Ohno-Machado L, Nadkarni P, Johnson K (2013). "Natural language processing: algorithms and tools to extract computable information from EHRs and from the biomedical literature". Journal of the American Medical Informatics Association. 20 (5): 805. doi:10.1136/amiajnl-2013-002214. PMC 3756279. PMID 23935077.
  5. ^ a b Uzuner Ö, South BR, Shen S, DuVall SL (2011). "2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text". Journal of the American Medical Informatics Association. 18 (5): 552–6. doi:10.1136/amiajnl-2011-000203. PMC 3168320. PMID 21685143.
  6. ^ a b Sun W, Rumshisky A, Uzuner O (2013). "Evaluating temporal relations in clinical text: 2012 i2b2 Challenge". Journal of the American Medical Informatics Association. 20 (5): 806–13. doi:10.1136/amiajnl-2013-001628. PMC 3756273. PMID 23564629.
  7. ^ Stubbs A, Kotfila C, Uzuner Ö (December 2015). "Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1". Journal of Biomedical Informatics. 58 Suppl: S11–9. doi:10.1016/j.jbi.2015.06.007. PMC 4989908. PMID 26225918.
  8. ^ Albright D, Lanfranchi A, Fredriksen A, Styler WF, Warner C, Hwang JD, Choi JD, Dligach D, Nielsen RD, Martin J, Ward W, Palmer M, Savova GK (2013). "Towards comprehensive syntactic and semantic annotations of the clinical narrative". Journal of the American Medical Informatics Association. 20 (5): 922–30. doi:10.1136/amiajnl-2012-001317. PMC 3756257. PMID 23355458.
  9. ^ Bada M, Eckert M, Evans D, Garcia K, Shipley K, Sitnikov D, Baumgartner WA, Cohen KB, Verspoor K, Blake JA, Hunter LE (July 2012). "Concept annotation in the CRAFT corpus". BMC Bioinformatics. 13 (1): 161. doi:10.1186/1471-2105-13-161. PMC 3476437. PMID 22776079.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  10. ^ Holzinger A, Jurisica I (2014), "Knowledge Discovery and Data Mining in Biomedical Informatics: The Future Is in Integrative, Interactive Machine Learning Solutions", Interactive Knowledge Discovery and Data Mining in Biomedical Informatics, Springer Berlin Heidelberg, pp. 1–18, doi:10.1007/978-3-662-43968-5_1, ISBN 9783662439678
  11. ^ Ratner A, Bach SH, Ehrenberg H, Fries J, Wu S, Ré C (November 2017). "Snorkel: Rapid Training Data Creation with Weak Supervision". Proceedings of the VLDB Endowment. 11 (3): 269–282. arXiv:1711.10160. Bibcode:2017arXiv171110160R. doi:10.14778/3157794.3157797. PMC 5951191. PMID 29770249.
  12. ^ Ren X, Wu Z, He W, Qu M, Voss CR, Ji H, Abdelzaher TF, Han J (2017-04-03). "Co Type". CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases. International World Wide Web Conferences Steering Committee. pp. 1015–1024. doi:10.1145/3038912.3052708. ISBN 9781450349130. S2CID 1724837.
  13. ^ a b Erhardt RA, Schneider R, Blaschke C (April 2006). "Status of text-mining techniques applied to biomedical text". Drug Discovery Today. 11 (7–8): 315–25. doi:10.1016/j.drudis.2006.02.011. PMID 16580973.
  14. ^ Milosevic N, Gregson C, Hernandez R, Nenadic G (February 2019). "A framework for information extraction from tables in biomedical literature". International Journal on Document Analysis and Recognition. 22 (1): 55–78. arXiv:1902.10031. Bibcode:2019arXiv190210031M. doi:10.1007/s10032-019-00317-0. S2CID 62880746.
  15. ^ Demner-Fushman D, Shooshan SE, Rodriguez L, Aronson AR, Lang F, Rogers W, Roberts K, Tonning J (January 2018). "A dataset of 200 structured product labels annotated for adverse drug reactions". Scientific Data. 5: 180001. Bibcode:2018NatSD...580001D. doi:10.1038/sdata.2018.1. PMC 5789866. PMID 29381145.
  16. ^ a b Agarwal S, Yu H (December 2010). "Detecting hedge cues and their scope in biomedical text with conditional random fields". Journal of Biomedical Informatics. 43 (6): 953–61. doi:10.1016/j.jbi.2010.08.003. PMC 2991497. PMID 20709188.
  17. ^ Vandenbussche PY, Cormont S, André C, Daniel C, Delahousse J, Charlet J, Lepage E (2013). "Implementation and management of a biomedical observation dictionary in a large healthcare information system". Journal of the American Medical Informatics Association. 20 (5): 940–6. doi:10.1136/amiajnl-2012-001410. PMC 3756262. PMID 23635601.
  18. ^ Jannot AS, Zapletal E, Avillach P, Mamzer MF, Burgun A, Degoulet P (June 2017). "The Georges Pompidou University Hospital Clinical Data Warehouse: A 8-years follow-up experience". International Journal of Medical Informatics. 102: 21–28. doi:10.1016/j.ijmedinf.2017.02.006. PMID 28495345.
  19. ^ Levy B. "Health Care's Semantics Challenge". www.fortherecordmag.com. Great Valley Publishing Company. Retrieved 2018-10-04.
  20. ^ Goodwin LK, Prather JC (2002). "Protecting patient privacy in clinical data mining". Journal of Healthcare Information Management. 16 (4): 62–7. PMID 12365302.
  21. ^ Tucker K, Branson J, Dilleen M, Hollis S, Loughlin P, Nixon MJ, Williams Z (July 2016). "Protecting patient privacy when sharing patient-level data from clinical trials". BMC Medical Research Methodology. 16 Suppl 1 (S1): 77. doi:10.1186/s12874-016-0169-4. PMC 4943495. PMID 27410040.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  22. ^ Graves S (2013). "Confidentiality, electronic health records, and the clinician". Perspectives in Biology and Medicine. 56 (1): 105–25. doi:10.1353/pbm.2013.0003. PMID 23748530. S2CID 25816887.
  23. ^ Leser U, Hakenberg J (2005-01-01). "What makes a gene name? Named entity recognition in the biomedical literature". Briefings in Bioinformatics. 6 (4): 357–369. doi:10.1093/bib/6.4.357. ISSN 1467-5463. PMID 16420734.
  24. ^ Krallinger M, Leitner F, Rabal O, Vazquez M, Oyarzabal J, Valencia A. "Overview of the chemical compound and drug name recognition (CHEMDNER) task" (PDF). Proceedings of the Fourth BioCreative Challenge Evaluation Workshop. 2: 6–37.
  25. ^ Jimeno A, Jimenez-Ruiz E, Lee V, Gaudan S, Berlanga R, Rebholz-Schuhmann D (April 2008). "Assessment of disease named entity recognition on a corpus of annotated sentences". BMC Bioinformatics. 9 Suppl 3 (Suppl 3): S3. doi:10.1186/1471-2105-9-s3-s3. PMC 2352871. PMID 18426548.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  26. ^ Habibi M, Weber L, Neves M, Wiegandt DL, Leser U (July 2017). "Deep learning with word embeddings improves biomedical named entity recognition". Bioinformatics. 33 (14): i37–i48. doi:10.1093/bioinformatics/btx228. PMC 5870729. PMID 28881963.
  27. ^ Cohen AM (2006). "An effective general purpose approach for automated biomedical document classification". AMIA ... Annual Symposium Proceedings. AMIA Symposium: 161–5. PMC 1839342. PMID 17238323.
  28. ^ a b Xu R, Wunsch DC (2010). "Clustering algorithms in biomedical research: a review". IEEE Reviews in Biomedical Engineering. 3: 120–54. doi:10.1109/rbme.2010.2083647. PMID 22275205. S2CID 206522771.
  29. ^ Rodriguez-Esteban R (December 2009). "Biomedical text mining and its applications". PLOS Computational Biology. 5 (12): e1000597. Bibcode:2009PLSCB...5E0597R. doi:10.1371/journal.pcbi.1000597. PMC 2791166. PMID 20041219.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  30. ^ Blake C (April 2010). "Beyond genes, proteins, and abstracts: Identifying scientific claims from full-text biomedical articles". Journal of Biomedical Informatics. 43 (2): 173–89. doi:10.1016/j.jbi.2009.11.001. PMID 19900574.
  31. ^ a b Alamri A, Stevensony M (2015). Automatic identification of potentially contradictory claims to support systematic reviews. IEEE. doi:10.1109/bibm.2015.7359808. ISBN 978-1-4673-6799-8. S2CID 28079483. {{cite book}}: |journal= ignored (help)
  32. ^ Fleuren WW, Alkema W (March 2015). "Application of text mining in the biomedical domain". Methods. 74: 97–106. doi:10.1016/j.ymeth.2015.01.015. PMID 25641519.
  33. ^ Karp PD (2016-01-01). "Can we replace curation with information extraction software?". Database. 2016: baw150. doi:10.1093/database/baw150. PMC 5199131. PMID 28025341.
  34. ^ Krallinger M, Valencia A, Hirschman L (2008). "Linking genes to literature: text mining, information extraction, and retrieval applications for biology". Genome Biology. 9 Suppl 2 (Suppl 2): S8. doi:10.1186/gb-2008-9-s2-s8. PMC 2559992. PMID 18834499.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  35. ^ Neves M, Leser U (March 2015). "Question answering for biology". Methods. 74: 36–46. doi:10.1016/j.ymeth.2014.10.023. PMID 25448292.
  36. ^ Semantics Scholar. (2020) "Cut through the clutter:[Open Access] Download the Coronavirus Open Research Dataset". Semantics Scholar website Retrieved 30 March 2020
  37. ^ Brennan, Patti. (24 March 2020). "Blog:How Does a Library Respond to a Global Health Crisis?". National Library of Medicine website Retrieved 30 March 2020.
  38. ^ Brainard, Jeffrey (13 May 2020). "Scientists are drowning in COVID-19 papers. Can new tools keep them afloat?". Science | AAAS. Retrieved 17 May 2020.
  39. ^ Uzuner O, Luo Y, Szolovits P (2007-09-01). "Evaluating the state-of-the-art in automatic de-identification". Journal of the American Medical Informatics Association. 14 (5): 550–63. doi:10.1197/jamia.m2444. PMC 1975792. PMID 17600094.
  40. ^ Uzuner O, Goldstein I, Luo Y, Kohane I (2008-01-01). "Identifying patient smoking status from medical discharge records". Journal of the American Medical Informatics Association. 15 (1): 14–24. doi:10.1197/jamia.m2408. PMC 2274873. PMID 17947624.
  41. ^ Uzuner O (2009). "Recognizing obesity and comorbidities in sparse data". Journal of the American Medical Informatics Association. 16 (4): 561–70. doi:10.1197/jamia.M3115. PMC 2705260. PMID 19390096.
  42. ^ Uzuner O, Solti I, Xia F, Cadag E (2010). "Community annotation experiment for ground truth generation for the i2b2 medication challenge". Journal of the American Medical Informatics Association. 17 (5): 519–23. doi:10.1136/jamia.2010.004200. PMC 2995684. PMID 20819855.
  43. ^ Uzuner O, Solti I, Cadag E (2010). "Extracting medication information from clinical text". Journal of the American Medical Informatics Association. 17 (5): 514–8. doi:10.1136/jamia.2010.003947. PMC 2995677. PMID 20819854.
  44. ^ Uzuner O, Bodnari A, Shen S, Forbush T, Pestian J, South BR (2012). "Evaluating the state of the art in coreference resolution for electronic medical records". Journal of the American Medical Informatics Association. 19 (5): 786–91. doi:10.1136/amiajnl-2011-000784. PMC 3422835. PMID 22366294.
  45. ^ Stubbs A, Uzuner Ö (December 2015). "Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/UTHealth corpus". Journal of Biomedical Informatics. 58 Suppl: S20–9. doi:10.1016/j.jbi.2015.07.020. PMC 4978170. PMID 26319540.
  46. ^ Stubbs A, Uzuner Ö (December 2015). "Annotating risk factors for heart disease in clinical narratives for diabetic patients". Journal of Biomedical Informatics. 58 Suppl: S78–91. doi:10.1016/j.jbi.2015.05.009. PMC 4978180. PMID 26004790.
  47. ^ Bunescu R, Ge R, Kate RJ, Marcotte EM, Mooney RJ, Ramani AK, Wong YW (February 2005). "Comparative experiments on learning information extractors for proteins and their interactions". Artificial Intelligence in Medicine. 33 (2): 139–55. CiteSeerX 10.1.1.10.2168. doi:10.1016/j.artmed.2004.07.016. PMID 15811782.
  48. ^ Islamaj Dogan R, Kim S, Chatr-Aryamontri A, Chang CS, Oughtred R, Rust J, Wilbur WJ, Comeau DC, Dolinski K, Tyers M (2017-01-01). "The BioC-BioGRID corpus: full text articles annotated for curation of protein-protein and genetic interactions". Database. 2017: baw147. doi:10.1093/database/baw147. PMC 5225395. PMID 28077563.
  49. ^ Hirschman L, Yeh A, Blaschke C, Valencia A (2005). "Overview of BioCreAtIvE: critical assessment of information extraction for biology". BMC Bioinformatics. 6 Suppl 1: S1. doi:10.1186/1471-2105-6-S1-S1. PMC 1869002. PMID 15960821.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  50. ^ Krallinger M, Morgan A, Smith L, Leitner F, Tanabe L, Wilbur J, Hirschman L, Valencia A (2008). "Evaluation of text-mining systems for biology: overview of the Second BioCreative community challenge". Genome Biology. 9 Suppl 2 (Suppl 2): S1. doi:10.1186/gb-2008-9-s2-s1. PMC 2559980. PMID 18834487.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  51. ^ Li J, Sun Y, Johnson RJ, Sciaky D, Wei CH, Leaman R, Davis AP, Mattingly CJ, Wiegers TC, Lu Z (2016). "BioCreative V CDR task corpus: a resource for chemical disease relation extraction". Database. 2016: baw068. doi:10.1093/database/baw068. PMC 4860626. PMID 27161011.
  52. ^ Pyysalo S, Ginter F, Heimonen J, Björne J, Boberg J, Järvinen J, Salakoski T (February 2007). "BioInfer: a corpus for information extraction in the biomedical domain". BMC Bioinformatics. 8 (1): 50. doi:10.1186/1471-2105-8-50. PMC 1808065. PMID 17291334.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  53. ^ Vincze V, Szarvas G, Farkas R, Móra G, Csirik J (November 2008). "The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes". BMC Bioinformatics. 9 Suppl 11 (Suppl 11): S9. doi:10.1186/1471-2105-9-s11-s9. PMC 2586758. PMID 19025695.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  54. ^ Schwartz AS, Hearst MA (2003). "A simple algorithm for identifying abbreviation definitions in biomedical text". Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing: 451–62. PMID 12603049.
  55. ^ Rosario B, Hearst MA (2005-10-06). "Multi-way relation classification". Multi-way relation classification: application to protein-protein interactions. Hlt '05. Association for Computational Linguistics. pp. 732–739. doi:10.3115/1220575.1220667. S2CID 902226.
  56. ^ Davis, Allan Peter; Grondin, Cynthia J; Johnson, Robin J; Sciaky, Daniela; McMorran, Roy; Wiegers, Jolene; Wiegers, Thomas C; Mattingly, Carolyn J (2019-01-08). "The Comparative Toxicogenomics Database: update 2019". Nucleic Acids Research. 47 (D1): D948–D954. doi:10.1093/nar/gky868. ISSN 0305-1048. PMC 6323936. PMID 30247620.
  57. ^ Verspoor K, Cohen KB, Lanfranchi A, Warner C, Johnson HL, Roeder C, Choi JD, Funk C, Malenkiy Y, Eckert M, Xue N, Baumgartner WA, Bada M, Palmer M, Hunter LE (August 2012). "A corpus of full-text journal articles is a robust evaluation tool for revealing differences in performance of biomedical natural language processing tools". BMC Bioinformatics. 13 (1): 207. doi:10.1186/1471-2105-13-207. PMC 3483229. PMID 22901054.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  58. ^ Kim JD, Ohta T, Tateisi Y, Tsujii J (2003-07-03). "GENIA corpus--a semantically annotated corpus for bio-textmining". Bioinformatics. 19 (Suppl 1): i180–i182. doi:10.1093/bioinformatics/btg1023. PMID 12855455.
  59. ^ "GENIA Project". www.geniaproject.org. Retrieved 2018-10-06.
  60. ^ Bachman JA, Gyori BM, Sorger PK (June 2018). "FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text mining". BMC Bioinformatics. 19 (1): 248. doi:10.1186/s12859-018-2211-5. PMC 6022344. PMID 29954318.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  61. ^ Vlachos A, Gasperin C (2006). "Bootstrapping and evaluating named entity recognition in the biomedical domain". BioNLP '06 Proceedings of the Workshop on Linking Natural Language Processing and Biology: Towards Deeper Biological Literature Analysis. BioNLP '06: 138–145. doi:10.3115/1567619.1567652.
  62. ^ Gasperin C, Karamanis N, Seal R (2007). "Annotation of anaphoric relations in biomedical full text articles using a domain-relevant scheme". Proceedings of DAARC 2007: 19–24.
  63. ^ Medlock B, Briscoe T (2007). "Weakly Supervised Learning for Hedge Classification in Scientific Literature" (PDF). Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics: 992–999.
  64. ^ Ding J, Berleant D, Nettleton D, Wurtele E (2001). "Mining MEDLINE: Abstracts, sentences, or phrases?". In Altman RB, Dunker AK, Hunter L, Lauderdale K, Klein TE (eds.). Pacific Symposium on Biocomputing 2002. World Scientific. pp. 326–337. CiteSeerX 10.1.1.385.6071. doi:10.1142/9789812799623_0031. ISBN 9789810247775. PMID 11928487. {{cite book}}: |journal= ignored (help)
  65. ^ Kim, Jin-Dong; Ohta, Tomoko; Tsuruoka, Yoshimasa; Tateisi, Yuka; Collier, Nigel (2004). "Introduction to the bio-entity recognition task at JNLPBA". Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and Its Applications - JNLPBA '04: 70. doi:10.3115/1567594.1567610.
  66. ^ "LLLchallenge". genome.jouy.inra.fr. Retrieved 2018-10-06.
  67. ^ "Medical Subject Headings - Home Page". www.nlm.nih.gov. Retrieved 2018-10-06.
  68. ^ Bodenreider O (January 2004). "The Unified Medical Language System (UMLS): integrating biomedical terminology". Nucleic Acids Research. 32 (Database issue): D267–70. doi:10.1093/nar/gkh061. PMC 308795. PMID 14681409.
  69. ^ "Metathesaurus". www.nlm.nih.gov. Retrieved 2018-10-07.
  70. ^ Johnson AE, Pollard TJ, Shen L, Lehman LW, Feng M, Ghassemi M, Moody B, Szolovits P, Celi LA, Mark RG (May 2016). "MIMIC-III, a freely accessible critical care database". Scientific Data. 3: 160035. Bibcode:2016NatSD...360035J. doi:10.1038/sdata.2016.35. PMC 4878278. PMID 27219127.
  71. ^ Savova GK, Chapman WW, Zheng J, Crowley RS (2011). "Anaphoric relations in the clinical narrative: corpus creation". Journal of the American Medical Informatics Association. 18 (4): 459–65. doi:10.1136/amiajnl-2011-000108. PMC 3128403. PMID 21459927.
  72. ^ Hersh W, Buckley C, Leone TJ, Hickam D (1994). OHSUMED: An Interactive Retrieval Evaluation and New Large Test Collection for Research. Springer London. pp. 192–201. doi:10.1007/978-1-4471-2099-5_20. ISBN 9783540198895. S2CID 15094383.
  73. ^ "Open Access Subset". www.ncbi.nlm.nih.gov. Retrieved 2018-10-06.
  74. ^ Nelson SJ, Zeng K, Kilbourne J, Powell T, Moore R (2011). "Normalized names for clinical drugs: RxNorm at 6 years". Journal of the American Medical Informatics Association. 18 (4): 441–8. doi:10.1136/amiajnl-2011-000116. PMC 3128404. PMID 21515544.
  75. ^ McCray AT (2003). "An upper-level ontology for the biomedical domain". Comparative and Functional Genomics. 4 (1): 80–4. doi:10.1002/cfg.255. PMC 2447396. PMID 18629109.
  76. ^ "The UMLS Semantic Network". semanticnetwork.nlm.nih.gov. Retrieved 2018-10-07.
  77. ^ McCray AT, Srinivasan S, Browne AC (1994). "Lexical methods for managing variation in biomedical terminologies". Proceedings. Symposium on Computer Applications in Medical Care: 235–9. PMC 2247735. PMID 7949926.
  78. ^ "The SPECIALIST NLP Tools". lexsrv3.nlm.nih.gov. Retrieved 2018-10-07.
  79. ^ Jimeno-Yepes AJ, McInnes BT, Aronson AR (June 2011). "Exploiting MeSH indexing in MEDLINE to generate a data set for word sense disambiguation". BMC Bioinformatics. 12 (1): 223. doi:10.1186/1471-2105-12-223. PMC 3123611. PMID 21635749.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  80. ^ "Word Sense Disambiguation (WSD) Test Collections". wsd.nlm.nih.gov. Retrieved 2018-10-07.
  81. ^ Franzén K, Eriksson G, Olsson F, Asker L, Lidén P, Cöster J (December 2002). "Protein names and how to find them". International Journal of Medical Informatics. 67 (1–3): 49–61. CiteSeerX 10.1.1.14.2183. doi:10.1016/s1386-5056(02)00052-7. PMID 12460631.
  82. ^ Mikolov T, Chen K, Corrado G, Dean J (2013-01-16). "Efficient Estimation of Word Representations in Vector Space". arXiv:1301.3781 [cs.CL].
  83. ^ "BioASQ Releases Continuous Space Word Vectors Obtained by Applying Word2Vec to PubMed Abstracts | bioasq.org". bioasq.org. Retrieved 2018-11-07.
  84. ^ "bio.nlplab.org". bio.nlplab.org. Retrieved 2018-11-07.
  85. ^ Asgari E, Mofrad MR (2015-11-10). "Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics". PLOS ONE. 10 (11): e0141287. arXiv:1503.05140. Bibcode:2015PLoSO..1041287A. doi:10.1371/journal.pone.0141287. PMC 4640716. PMID 26555596.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  86. ^ Banerjee I, Madhavan S, Goldman RE, Rubin DL (2017). "Intelligent Word Embeddings of Free-Text Radiology Reports". AMIA ... Annual Symposium Proceedings. AMIA Symposium. 2017: 411–420. arXiv:1711.06968. Bibcode:2017arXiv171106968B. PMC 5977573. PMID 29854105.
  87. ^ a b Badal VD, Kundrotas PJ, Vakser IA (December 2015). "Text Mining for Protein Docking". PLOS Computational Biology. 11 (12): e1004630. Bibcode:2015PLSCB..11E4630B. doi:10.1371/journal.pcbi.1004630. PMC 4674139. PMID 26650466.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  88. ^ Papanikolaou N, Pavlopoulos GA, Theodosiou T, Iliopoulos I (March 2015). "Protein-protein interaction predictions using text mining methods". Methods. 74: 47–53. doi:10.1016/j.ymeth.2014.10.026. PMID 25448298.
  89. ^ Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, Jensen LJ, von Mering C (January 2017). "The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible". Nucleic Acids Research. 45 (D1): D362–D368. doi:10.1093/nar/gkw937. PMC 5210637. PMID 27924014.
  90. ^ a b Liem DA, Murali S, Sigdel D, Shi Y, Wang X, Shen J, Choi H, Caufield JH, Wang W, Ping P, Han J (October 2018). "Phrase mining of textual data to analyze extracellular matrix protein patterns across cardiovascular disease". American Journal of Physiology. Heart and Circulatory Physiology. 315 (4): H910–H924. doi:10.1152/ajpheart.00175.2018. PMC 6230912. PMID 29775406.
  91. ^ Kankar P, Adak S, Sarkar A, Murari K, Sharma G (11 April 2002). MedMeSH summarizer: text mining for gene clusters. InProceedings of the 2002 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics. pp. 548–565. CiteSeerX 10.1.1.215.6230. doi:10.1137/1.9781611972726.32. ISBN 978-0-89871-517-0.
  92. ^ Pyysalo S, Airola A, Heimonen J, Björne J, Ginter F, Salakoski T (April 2008). "Comparative analysis of five protein-protein interaction corpora". BMC Bioinformatics. 9 Suppl 3 (Suppl 3): S6. doi:10.1186/1471-2105-9-s3-s6. PMC 2349296. PMID 18426551.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  93. ^ Kim S, Kwon D, Shin SY, Wilbur WJ (February 2012). "PIE the search: searching PubMed literature for protein interaction information". Bioinformatics. 28 (4): 597–8. doi:10.1093/bioinformatics/btr702. PMC 3278758. PMID 22199390.
  94. ^ Yu S, Van Vooren S, Tranchevent LC, De Moor B, Moreau Y (August 2008). "Comparison of vocabularies, representations and ranking algorithms for gene prioritization by text mining". Bioinformatics. 24 (16): i119–25. doi:10.1093/bioinformatics/btn291. PMID 18689812.
  95. ^ Hulsegge I, Woelders H, Smits M, Schokker D, Jiang L, Sørensen P (May 2013). "Prioritization of candidate genes for cattle reproductive traits, based on protein-protein interactions, gene expression, and text-mining". Physiological Genomics. 45 (10): 400–6. doi:10.1152/physiolgenomics.00172.2012. PMID 23572538.
  96. ^ Krallinger M, Leitner F, Valencia A (2010). "Analysis of biological processes and diseases using text mining approaches". Bioinformatics Methods in Clinical Research. Methods in Molecular Biology. Vol. 593. pp. 341–82. doi:10.1007/978-1-60327-194-3_16. ISBN 978-1-60327-193-6. PMID 19957157.
  97. ^ Tao F, Zhuang H, Yu CW, Wang Q, Cassidy T, Kaplan LR, Voss CR, Han J (2016). "Multi-Dimensional, Phrase-Based Summarization in Text Cubes" (PDF). IEEE Data Eng. Bull. 39 (3): 74–84.
  98. ^ Thomas P, Starlinger J, Vowinkel A, Arzt S, Leser U (July 2012). "GeneView: a comprehensive semantic search engine for PubMed". Nucleic Acids Research. 40 (Web Server issue): W585–91. doi:10.1093/nar/gks563. PMC 3394277. PMID 22693219.
  99. ^ Brown P, Zhou Y (September 2017). "Biomedical literature: Testers wanted for article search tool". Nature. 549 (7670): 31. Bibcode:2017Natur.549...31B. doi:10.1038/549031c. PMID 28880292.
  100. ^ Ohno-Machado L, Sansone SA, Alter G, Fore I, Grethe J, Xu H, Gonzalez-Beltran A, Rocca-Serra P, Gururaj AE, Bell E, Soysal E, Zong N, Kim HE (May 2017). "Finding useful data across multiple biomedical data repositories using DataMed". Nature Genetics. 49 (6): 816–819. doi:10.1038/ng.3864. PMC 6460922. PMID 28546571.
  101. ^ Perez-Riverol Y, Bai M, da Veiga Leprevost F, Squizzato S, Park YM, Haug K, et al. (May 2017). "Discovering and linking public omics data sets using the Omics Discovery Index". Nature Biotechnology. 35 (5): 406–409. doi:10.1038/nbt.3790. PMC 5831141. PMID 28486464.
  102. ^ Ide NC, Loane RF, Demner-Fushman D (2007-05-01). "Essie: a concept-based search engine for structured biomedical text". Journal of the American Medical Informatics Association. 14 (3): 253–63. doi:10.1197/jamia.m2233. PMC 2244877. PMID 17329729.
  103. ^ Lee HJ, Dang TC, Lee H, Park JC (July 2014). "OncoSearch: cancer gene search engine with literature evidence". Nucleic Acids Research. 42 (Web Server issue): W416–21. doi:10.1093/nar/gku368. PMC 4086113. PMID 24813447.
  104. ^ Jenssen TK, Laegreid A, Komorowski J, Hovig E (May 2001). "A literature network of human genes for high-throughput analysis of gene expression". Nature Genetics. 28 (1): 21–8. doi:10.1038/ng0501-21. PMID 11326270. S2CID 8889284.
  105. ^ Masys DR (May 2001). "Linking microarray data to the literature". Nature Genetics. 28 (1): 9–10. doi:10.1038/ng0501-9. PMID 11326264. S2CID 52848745.
  106. ^ Doms A, Schroeder M (July 2005). "GoPubMed: exploring PubMed with the Gene Ontology". Nucleic Acids Research. 33 (Web Server issue): W783–6. doi:10.1093/nar/gki470. PMC 1160231. PMID 15980585.
  107. ^ Wang Y, Wang L, Rastegar-Mojarad M, Moon S, Shen F, Afzal N, Liu S, Zeng Y, Mehrabi S, Sohn S, Liu H (January 2018). "Clinical information extraction applications: A literature review". Journal of Biomedical Informatics. 77: 34–49. doi:10.1016/j.jbi.2017.11.011. PMC 5771858. PMID 29162496.
  108. ^ Friedman C (1997). "Towards a comprehensive medical language processing system: methods and issues". Proceedings: 595–9. PMC 2233560. PMID 9357695.
  109. ^ Savova GK, Masanz JJ, Ogren PV, Zheng J, Sohn S, Kipper-Schuler KC, Chute CG (2010). "Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications". Journal of the American Medical Informatics Association. 17 (5): 507–13. doi:10.1136/jamia.2009.001560. PMC 2995668. PMID 20819853.
  110. ^ Soysal E, Wang J, Jiang M, Wu Y, Pakhomov S, Liu H, Xu H (2018). "CLAMP - a toolkit for efficiently building customized clinical natural language processing pipelines". Journal of the American Medical Informatics Association. 25 (3): 331–336. doi:10.1093/jamia/ocx132. PMC 7378877. PMID 29186491.
  111. ^ Fries J, Wu S, Ratner A, Ré C (2017-04-20). "SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data". arXiv:1704.06360 [cs.CL].
  112. ^ Ye Z, Tafti AP, He KY, Wang K, He MM (2016-09-29). "SparkText: Biomedical Text Mining on Big Data Framework". PLOS ONE. 11 (9): e0162721. Bibcode:2016PLoSO..1162721Y. doi:10.1371/journal.pone.0162721. PMC 5042555. PMID 27685652.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  113. ^ Tseytlin E, Mitchell K, Legowski E, Corrigan J, Chavan G, Jacobson RS (January 2016). "NOBLE - Flexible concept recognition for large-scale biomedical natural language processing". BMC Bioinformatics. 17 (1): 32. doi:10.1186/s12859-015-0871-y. PMC 4712516. PMID 26763894.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  114. ^ "BioNLP - ACL Anthology". aclanthology.coli.uni-saarland.de. Retrieved 2018-10-17.
  115. ^ "ISMB Proceedings". www.iscb.org. Retrieved 2018-10-18.
  116. ^ "IEEE Xplore - Conference Home Page". ieeexplore.ieee.org. Retrieved 2018-11-08.
  117. ^ "dblp: CIKM". dblp.uni-trier.de. Retrieved 2018-10-17.
  118. ^ "PSB Proceedings". psb.stanford.edu. Retrieved 2018-10-18.
  119. ^ "dblp: Practical Applications of Computational Biology & Bioinformatics". dblp.org. Retrieved 2018-10-17.
  120. ^ "Text REtrieval Conference (TREC) Proceedings". trec.nist.gov. Retrieved 2018-10-17.

Further reading

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