Biomedical text mining explained

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 domain. As a field of research, biomedical text mining incorporates ideas from natural language processing, bioinformatics, medical informatics and computational linguistics. The strategies in this field have been applied to the biomedical literature available through services such as PubMed.

In recent years, the scientific literature has shifted to electronic publishing but the volume of information available can be overwhelming. This revolution of publishing has caused a high demand for text mining techniques. Text mining offers information retrieval (IR) and entity recognition (ER).[1] IR allows the retrieval of relevant papers according to the topic of interest, e.g. through PubMed. ER is practiced when certain biological terms are recognized (e.g. proteins or genes) for further processing.

Considerations

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

Availability of annotated text data

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. This is a concern in environments where clinical decision support is expected to be informative and accurate. A comprehensive overview of the development and uptake of NLP methods applied to free-text clinical notes related to chronic diseasesis presented in.[17]

Interoperability with clinical systems

New text mining systems must work with existing standards, electronic medical records, and databases. Methods for interfacing with clinical systems such as LOINC have been developed[18] but require extensive organizational effort to implement and maintain.[19] [20]

Patient privacy

Text mining systems operating with private medical data must respect its security and ensure it is rendered anonymous where appropriate.[21] [22] [23]

Processes

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

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,[24] chemical compounds and drugs,[25] and disease names[26] 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.[27] [28]

Document classification and clustering

Biomedical documents may be classified or clustered based on their contents and topics. In classification, document categories are specified manually,[29] while in clustering, documents form algorithm-dependent, distinct groups.[30] 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.

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.[31]

Hedge cue detection

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

Claim detection

Multiple researchers have developed methods to identify specific scientific claims from literature.[32] [33] 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.

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.[34] 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.[35] [36]

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.[37]

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[38] of the Allen Institute for AI.[39] Other participants include Google, Microsoft Research, the Center for Security and Emerging Technology, and the Chan Zuckerberg Initiative.[40]

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
2019 Bacteria BiotopeBioNLP-OSTAnnotated scientific and textbook texts to recognize mentions of microorganisms, microbial biotopes and phenotypes, to normalize these mentions according to the knowledge resources of the field, and to extract the relationships between them.Yes[41]
2006 i2b2 Deidentification and Smoking Challengei2b2889 de-identified medical discharge summaries annotated for patient identification and smoking status features.Yes, with registration[42] [43]
2008 i2b2 Obesity Challengei2b21,237 de-identified medical discharge summaries annotated for presence or absence of comorbidities of obesity.Yes, with registration[44]
2009 i2b2 Medication Challengei2b21,243 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[45] [46]
2010 i2b2 Relations Challengei2b2Medical 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
2011 i2b2 Coreference Challengei2b2978 de-identified medical discharge summaries, progress notes, and other clinical reports annotated with concepts and coreferences. Includes the ODIE corpus.Yes, with registration[47]
2012 i2b2 Temporal Relations Challengei2b2310 de-identified medical discharge summaries annotated for events and temporal relations.Yes, with registration
2014 i2b2 De-identification Challengei2b21,304 de-identified longitudinal medical records annotated for protected health information (PHI).Yes, with registration[48]
2014 i2b2 Heart Disease Risk Factors Challengei2b21,304 de-identified longitudinal medical records annotated for risk factors for cardiac artery disease.Yes, with registration[49]
AIMedBunescu et al.200 abstracts annotated for protein–protein interactions, as well as negative example abstracts containing no protein-protein interactions.Yes[50]
BioC-BioGRIDBioCreAtIvE120 full text research articles annotated for protein–protein interactions.Yes[51]
BioCreAtIvE 1BioCreAtIvE15,000 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[52]
BioCreAtIvE 2BioCreAtIvE15,000 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[53]
BioCreative V CDR Task Corpus (BC5CDR)BioCreAtIvE1,500 articles (title and abstract) published in 2014 or later, annotated for 4,409 chemicals, 5,818 diseases and 3116 chemical–disease interactions. Yes[54]
BioInferPyysalo et al.1,100 sentences from biomedical research abstracts annotated for relationships, named entities, and syntactic dependencies.No[55]
BioScopeVincze et al.1,954 clinical reports, 9 papers, and 1,273 abstracts annotated for linguistic scope and terms denoting negation or uncertainty.Yes[56]
BioText Recognizing Abbreviation DefinitionsBioText Project1,000 abstracts on the subject of "yeast", annotated for abbreviations and their meanings. Yes[57]
BioText Protein–Protein Interaction DataBioText Project1,322 sentences describing protein–protein interactions between HIV-1 and human proteins, annotated with interaction types.Yes[58]
Comparative Toxicogenomics DatabaseDavis et al.A database of manually-curated associations between chemicals, gene products, phenotypes, diseases, and environmental exposures.Yes[59]
CRAFTVerspoor et al.97 full-text biomedical publications annotated with linguistic structures and biological conceptsYes[60]
GENIA CorpusGENIA Project1,999 biomedical research abstracts on the topics "human", "blood cells", and "transcription factors", annotated for parts of speech, syntax, terms, events, relations, and coreferences.Yes[61] [62]
FamPlexBachman et al.Protein names and families linked to unique identifiers. Includes affix sets.Yes[63]
FlySlip AbstractsFlySlip82 research abstracts on Drosophila annotated with gene names.Yes[64]
FlySlip Full PapersFlySlip5 research papers on Drosophila annotated with anaphoric relations between noun phrases referring to genes and biologically related entities.Yes[65]
FlySlip Speculative SentencesFlySlipMore than 1,500 sentences annotated as speculative or not speculative. Includes annotations of clauses.Yes[66]
IEPADing et al.486 sentences from biomedical research abstracts annotated for pairs of co-occurring chemicals, including proteins.No[67]
JNLPBA corpusKim et al.An extended version of version 3 of the GENIA corpus for NER tasks.No[68]
Learning Language in Logic (LLL)Nédellec et al.77 sentences from research articles about the bacterium Bacillus subtilis, annotated for protein–gene interactions.Yes[69]
Medical Subject Headings (MeSH)National Library of MedicineHierarchically-organized terminology for indexing and cataloging biomedical documents.Yes[70]
MetathesaurusNational Library of Medicine / UMLS3.67 million concepts and 14 million concept names, mapped between more than 200 sources of biomedical vocabulary and identifiers.Yes, with UMLS License Agreement[71] [72]
MIMIC-IIIMIT Lab for Computational Physiologyde-identified data associated with 53,423 distinct hospital admissions for adult patients.Requires training and formal access request[73]
ODIE CorpusSavova et al.180 clinical notes annotated with 5,992 coreference pairs.No[74]
OHSUMEDHersh et al.348,566 biomedical research abstracts and indexing information from MEDLINE, including MeSH (as of 1991).Yes[75]
PMC Open Access SubsetNational Library of Medicine / PubMed CentralMore than 2 million research articles, updated weekly.Yes[76]
RxNormNational Library of Medicine / UMLSNormalized 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[77]
Semantic NetworkNational Library of Medicine / UMLSLists of 133 semantic types and 54 semantic relationships covering biomedical concepts and vocabulary.Yes, with UMLS License Agreement[78] [79]
SPECIALIST LexiconNational Library of Medicine / UMLSA syntactic lexicon of biomedical and general English.Yes[80] [81]
Word Sense Disambiguation (WSD)National Library of Medicine / UMLS203 ambiguous words and 37,888 automatically extracted instances of their use in biomedical research publications. Yes, with UMLS License Agreement[82] [83]
YapexFranzén et al.200 biomedical research abstracts annotated with protein names.No[84]

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[85] or variants of word2vec.

Biomedical word embeddings!Set Name!Authors or Group!Contents and Source!Citation
BioASQword2vecBioASQVectors produced by word2vec from 10,876,004 English PubMed abstracts.[86]
bio.nlplab.org resourcesPyysalo et al.A collection of word vectors produced by different approaches, trained on text from PubMed and PubMed Central.[87]
BioVecAsgari and MofradVectors for gene and protein sequences, trained using Swiss-Prot.[88]
RadiologyReportEmbeddingBanerjee et al.Vectors produced by word2vec from the text of 10,000 radiology reports.[89]

Applications

Text mining applications in the biomedical field include computational approaches to assist with studies in protein docking, protein interactions,[90] [91] and protein-disease associations.[92] Text mining techniques have several advantages over traditional manual curation for identifying associations. Text mining algorithms can identify and extract information from a vast amount of literature, and more efficiently than manual curation. This includes the integration of data from different sources, including literature, databases, and experimental results. These algorithms have transformed the process of identifying and prioritizing novel genes and gene-disease associations that have previously been overlooked.[93] These methods are the foundation to facilitate systematic searches of overlooked scientific and biomedical  literature which could carry significant association between research. The combination of information can stem new discoveries and hypotheses especially with the integration of datasets. It must be noted that the quality of the database is as important as the size of it. Promising text mining methods such as iProLINK (integrated Protein Literature Information and Knowledge) have been developed to curate data sources that can aid text mining research in areas of bibliography mapping, annotation extraction, protein named entity recognition, and protein ontology development.[94] Curated databases such as UniProt can accelerate the accessibility of targeted information not only for genetic sequences, but also for literature and phylogeny.

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.[95]

Protein interactions

Automatic extraction of protein interactions[96] and associations of proteins to functional concepts (e.g. gene ontology terms) has been explored. The search engine PIE was developed to identify and return protein-protein interaction mentions from MEDLINE-indexed articles.[97] The extraction of kinetic parameters from text or the subcellular location of proteins have also been addressed by information extraction and text mining technology.

Gene-disease associations

Computational gene prioritization is an essential step in understanding the genetic basis of diseases, particularly within genetic linkage analysis. Text mining and other computational tools extract relevant information, including gene-disease associations, among others, from numerous data sources, then apply different ranking algorithms to prioritize the genes based on their relevance to the specific disease.[98] Text mining and gene prioritization allow researchers to focus their efforts on the most promising candidates for further research.

Computational tools for gene prioritization continue to be developed and analyzed. One group studied the performance of various text-mining techniques for disease gene prioritization. They investigated different domain vocabularies, text representation schemes, and ranking algorithms in order to find the best approach for identifying disease-causing genes to establish a benchmark.[99]

Gene-trait associations

An agricultural genomics group identified genes related to bovine reproductive traits using text mining, among other approaches.[100]

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),[101] 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.

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,[102] and APSE[103] Similarly, search engines and indexing systems specific for biomedical data have been developed, including DataMed[104] and OmicsDI.[105]

Some search engines, such as Essie,[106] OncoSearch,[107] PubGene,[108] [109] and GoPubMed[110] 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 and difficult to search, leading to challenges with patient care.[111] Numerous complete systems and tools have been developed to analyse these free-text portions.[112] The MedLEE system was originally developed for analysis of chest radiology reports but later extended to other report topics.[113] The clinical Text Analysis and Knowledge Extraction System, or cTAKES, annotates clinical text using a dictionary of concepts.[114] The CLAMP system offers similar functionality with a user-friendly interface.[115]

Frameworks

Computational frameworks have been developed to rapidly build tools for biomedical text mining tasks. SwellShark[116] 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[117] 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.[118]

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 meetingplenary session and as part of the BioNLP workshop
ACL BioNLP workshop[119]
American Medical Informatics Association (AMIA) annual meetingin plenary session
Intelligent Systems for Molecular Biology (ISMB)in plenary session and in the BioLINK and Bio-ontologies workshops[120]
International Conference on Bioinformatics and Biomedicine (BIBM)[121]
International Conference on Information and Knowledge Management (CIKM)within International Workshop on Data and Text Mining in Biomedical Informatics (DTMBIO)[122]
North American Association for Computational Linguistics (NAACL) annual meetingplenary session and as part of the BioNLP workshop
Pacific Symposium on Biocomputing (PSB)in plenary session[123]
Practical Applications of Computational Biology & Bioinformatics (PACBB)[124]
Text REtrieval Conference (TREC)formerly as part of TREC Genomics track; as of 2018 part of Precision Medicine Track[125]

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.

Further reading

External links

Notes and References

  1. Jensen . Lars Juhl . Saric . Jasmin . Bork . Peer . February 2006 . Literature mining for the biologist: from information retrieval to biological discovery . Nature Reviews Genetics . en . 7 . 2 . 119–129 . 10.1038/nrg1768 . 16418747 . 423509 . 1471-0056.
  2. Book: Danescu-Niculescu-Mizil C, Lee L . 2011. Chameleons in Imagined Conversations: A New Approach to Understanding Coordination of Linguistic Style in Dialogs. CMCL '11. 76–87. 978-1-932432-95-4 . 2011arXiv1106.3077D. 1106.3077.
  3. Book: McAuley J, Leskovec J . Proceedings of the 7th ACM conference on Recommender systems . Hidden factors and hidden topics: Understanding rating dimensions with review text . 6440341. 2013-10-12. ACM . 165–172 . 10.1145/2507157.2507163. 978-1-4503-2409-0 .
  4. Ohno-Machado L, Nadkarni P, Johnson K . 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 . 2013 . 23935077 . 3756279 . 10.1136/amiajnl-2013-002214 .
  5. Uzuner Ö, South BR, Shen S, DuVall SL . 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text . Journal of the American Medical Informatics Association . 18 . 5 . 552–6 . 2011 . 21685143 . 3168320 . 10.1136/amiajnl-2011-000203 .
  6. Sun W, Rumshisky A, Uzuner O . Evaluating temporal relations in clinical text: 2012 i2b2 Challenge . Journal of the American Medical Informatics Association . 20 . 5 . 806–13 . 2013 . 23564629 . 3756273 . 10.1136/amiajnl-2013-001628 .
  7. Stubbs A, Kotfila C, Uzuner Ö . 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 . December 2015 . 26225918 . 4989908 . 10.1016/j.jbi.2015.06.007 .
  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 . Towards comprehensive syntactic and semantic annotations of the clinical narrative . Journal of the American Medical Informatics Association . 20 . 5 . 922–30 . 2013 . 23355458 . 3756257 . 10.1136/amiajnl-2012-001317 .
  9. Bada M, Eckert M, Evans D, Garcia K, Shipley K, Sitnikov D, Baumgartner WA, Cohen KB, Verspoor K, Blake JA, Hunter LE . Concept annotation in the CRAFT corpus . BMC Bioinformatics . 13 . 1 . 161 . July 2012 . 22776079 . 3476437 . 10.1186/1471-2105-13-161 . free .
  10. Book: Holzinger A, Jurisica I . Knowledge Discovery and Data Mining in Biomedical Informatics: The Future is in Integrative, Interactive Machine Learning Solutions. 2014. Interactive Knowledge Discovery and Data Mining in Biomedical Informatics. Lecture Notes in Computer Science. 8401. 1–18. Springer Berlin Heidelberg. en. 10.1007/978-3-662-43968-5_1. 9783662439678.
  11. Ratner A, Bach SH, Ehrenberg H, Fries J, Wu S, Ré C . Snorkel: Rapid Training Data Creation with Weak Supervision . Proceedings of the VLDB Endowment . 11 . 3 . 269–282 . November 2017 . 29770249 . 5951191 . 10.14778/3157794.3157797 . 1711.10160 . 2017arXiv171110160R .
  12. Book: Ren X, Wu Z, He W, Qu M, Voss CR, Ji H, Abdelzaher TF, Han J. Proceedings of the 26th International Conference on World Wide Web . CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases . Tarek Abdelzaher . 1724837. 2017-04-03. http://dl.acm.org/citation.cfm?id=3038912.3052708. International World Wide Web Conferences Steering Committee. 1015–1024. 10.1145/3038912.3052708. 9781450349130. WWW '17.
  13. Erhardt RA, Schneider R, Blaschke C . Status of text-mining techniques applied to biomedical text . Drug Discovery Today . 11 . 7–8 . 315–25 . April 2006 . 16580973 . 10.1016/j.drudis.2006.02.011 .
  14. Milosevic N, Gregson C, Hernandez R, Nenadic G . A framework for information extraction from tables in biomedical literature . International Journal on Document Analysis and Recognition . 22 . 1 . 55–78 . February 2019 . 10.1007/s10032-019-00317-0 . 2019arXiv190210031M . 1902.10031 . 62880746 .
  15. Demner-Fushman D, Shooshan SE, Rodriguez L, Aronson AR, Lang F, Rogers W, Roberts K, Tonning J . A dataset of 200 structured product labels annotated for adverse drug reactions . En . Scientific Data . 5 . 180001 . January 2018 . 29381145 . 5789866 . 10.1038/sdata.2018.1 . 2018NatSD...580001D .
  16. Agarwal S, Yu H . Detecting hedge cues and their scope in biomedical text with conditional random fields . Journal of Biomedical Informatics . 43 . 6 . 953–61 . December 2010 . 20709188 . 2991497 . 10.1016/j.jbi.2010.08.003 .
  17. Sheikhalishahi S, Miotto R, Dudley JT, Lavelli A, Rinaldi F, Osmani V . Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review . JMIR Med Inform . 7 . 2 . e12239 . April 2019 . 31066697 . 6528438 . 10.2196/12239 . free .
  18. Vandenbussche PY, Cormont S, André C, Daniel C, Delahousse J, Charlet J, Lepage E . 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 . 2013 . 23635601 . 3756262 . 10.1136/amiajnl-2012-001410 .
  19. Jannot AS, Zapletal E, Avillach P, Mamzer MF, Burgun A, Degoulet P . The Georges Pompidou University Hospital Clinical Data Warehouse: A 8-years follow-up experience . International Journal of Medical Informatics . 102 . 21–28 . June 2017 . 28495345 . 10.1016/j.ijmedinf.2017.02.006 .
  20. Web site: Health Care's Semantics Challenge. Levy B . www.fortherecordmag.com. Great Valley Publishing Company . 2018-10-04.
  21. Goodwin LK, Prather JC . Protecting patient privacy in clinical data mining . Journal of Healthcare Information Management . 16 . 4 . 62–7 . 2002 . 12365302 .
  22. Tucker K, Branson J, Dilleen M, Hollis S, Loughlin P, Nixon MJ, Williams Z . Protecting patient privacy when sharing patient-level data from clinical trials . BMC Medical Research Methodology . 16 . S1 . 77 . July 2016 . 27410040 . 4943495 . 10.1186/s12874-016-0169-4 . free .
  23. Graves S . 25816887 . Confidentiality, electronic health records, and the clinician . Perspectives in Biology and Medicine . 56 . 1 . 105–25 . 2013 . 23748530 . 10.1353/pbm.2013.0003 .
  24. 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. 10.1093/bib/6.4.357. 16420734. 1467-5463. free.
  25. Krallinger M, Leitner F, Rabal O, Vazquez M, Oyarzabal J, Valencia A . Overview of the chemical compound and drug name recognition (CHEMDNER) task. . Proceedings of the Fourth BioCreative Challenge Evaluation Workshop . 2 . 6–37 .
  26. Jimeno A, Jimenez-Ruiz E, Lee V, Gaudan S, Berlanga R, Rebholz-Schuhmann D . Assessment of disease named entity recognition on a corpus of annotated sentences . BMC Bioinformatics . 9 . Suppl 3 . S3 . April 2008 . 18426548 . 2352871 . 10.1186/1471-2105-9-s3-s3 . free .
  27. Habibi M, Weber L, Neves M, Wiegandt DL, Leser U . Deep learning with word embeddings improves biomedical named entity recognition . Bioinformatics . 33 . 14 . i37–i48 . July 2017 . 28881963 . 5870729 . 10.1093/bioinformatics/btx228 .
  28. Furrer L, Cornelius J, Rinaldi F . Parallel sequence tagging for concept recognition . BMC Bioinformatics . 22 . Suppl 1 . 623 . March 2022 . 35331131 . 8943923 . 10.1186/s12859-021-04511-y . free .
  29. Cohen AM . An effective general purpose approach for automated biomedical document classification . AMIA ... Annual Symposium Proceedings. AMIA Symposium . 161–5 . 2006 . 2006 . 17238323 . 1839342 .
  30. Xu R, Wunsch DC . Clustering algorithms in biomedical research: a review . en-US . IEEE Reviews in Biomedical Engineering . 3 . 120–54 . 2010 . 22275205 . 10.1109/rbme.2010.2083647 . 206522771 .
  31. Rodriguez-Esteban R . Biomedical text mining and its applications . PLOS Computational Biology . 5 . 12 . e1000597 . December 2009 . 20041219 . 2791166 . 10.1371/journal.pcbi.1000597 . 2009PLSCB...5E0597R . free .
  32. Blake C . Beyond genes, proteins, and abstracts: Identifying scientific claims from full-text biomedical articles . Journal of Biomedical Informatics . 43 . 2 . 173–89 . April 2010 . 19900574 . 10.1016/j.jbi.2009.11.001 . free .
  33. Book: Alamri A, Stevensony M . 2015. Automatic identification of potentially contradictory claims to support systematic reviews . 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 930–937 . IEEE. 10.1109/bibm.2015.7359808. 978-1-4673-6799-8. 28079483.
  34. Fleuren WW, Alkema W . Application of text mining in the biomedical domain . Methods . 74 . 97–106 . March 2015 . 25641519 . 10.1016/j.ymeth.2015.01.015 .
  35. Karp PD . Can we replace curation with information extraction software? . Database . 2016 . baw150 . 2016-01-01 . 28025341 . 5199131 . 10.1093/database/baw150 .
  36. Krallinger M, Valencia A, Hirschman L . Linking genes to literature: text mining, information extraction, and retrieval applications for biology . En . Genome Biology . 9 . Suppl 2 . S8 . 2008 . 18834499 . 2559992 . 10.1186/gb-2008-9-s2-s8 . free .
  37. Neves M, Leser U . Question answering for biology . Methods . 74 . 36–46 . March 2015 . 25448292 . 10.1016/j.ymeth.2014.10.023 .
  38. Semantics Scholar. (2020) "Cut through the clutter:[Open Access] Download the Coronavirus Open Research Dataset". Semantics Scholar website Retrieved 30 March 2020
  39. 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.
  40. News: Brainard J . Scientists are drowning in COVID-19 papers. Can new tools keep them afloat? . 17 May 2020 . Science AAAS . 13 May 2020 . en.
  41. Bossy R, Deléger L, Chaix E, Ba M, Nédellec C . Bacteria biotope at BioNLP open shared tasks 2019 . Proceedings of the 5th workshop on BioNLP open shared tasks . 121–131 . Association for Computational Linguistics . 2019 . 10.18653/v1/D19-5719. free .
  42. Uzuner O, Luo Y, Szolovits P . Evaluating the state-of-the-art in automatic de-identification . Journal of the American Medical Informatics Association . 14 . 5 . 550–63 . 2007-09-01 . 17600094 . 1975792 . 10.1197/jamia.m2444 .
  43. Uzuner O, Goldstein I, Luo Y, Kohane I . Identifying patient smoking status from medical discharge records . Journal of the American Medical Informatics Association . 15 . 1 . 14–24 . 2008-01-01 . 17947624 . 2274873 . 10.1197/jamia.m2408 .
  44. Uzuner O . Recognizing obesity and comorbidities in sparse data . Journal of the American Medical Informatics Association . 16 . 4 . 561–70 . 2009 . 19390096 . 2705260 . 10.1197/jamia.M3115 .
  45. Uzuner O, Solti I, Xia F, Cadag E . Community annotation experiment for ground truth generation for the i2b2 medication challenge . Journal of the American Medical Informatics Association . 17 . 5 . 519–23 . 2010 . 20819855 . 2995684 . 10.1136/jamia.2010.004200 .
  46. Uzuner O, Solti I, Cadag E . Extracting medication information from clinical text . Journal of the American Medical Informatics Association . 17 . 5 . 514–8 . 2010 . 20819854 . 2995677 . 10.1136/jamia.2010.003947 .
  47. Uzuner O, Bodnari A, Shen S, Forbush T, Pestian J, South BR . Evaluating the state of the art in coreference resolution for electronic medical records . Journal of the American Medical Informatics Association . 19 . 5 . 786–91 . 2012 . 22366294 . 3422835 . 10.1136/amiajnl-2011-000784 .
  48. Stubbs A, Uzuner Ö . Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/UTHealth corpus . Journal of Biomedical Informatics . 58 . Suppl . S20–9 . December 2015 . 26319540 . 4978170 . 10.1016/j.jbi.2015.07.020 .
  49. Stubbs A, Uzuner Ö . Annotating risk factors for heart disease in clinical narratives for diabetic patients . Journal of Biomedical Informatics . 58 . Suppl . S78–91 . December 2015 . 26004790 . 4978180 . 10.1016/j.jbi.2015.05.009 .
  50. Bunescu R, Ge R, Kate RJ, Marcotte EM, Mooney RJ, Ramani AK, Wong YW . Comparative experiments on learning information extractors for proteins and their interactions . Artificial Intelligence in Medicine . 33 . 2 . 139–55 . February 2005 . 15811782 . 10.1016/j.artmed.2004.07.016 . 10.1.1.10.2168 .
  51. Islamaj Dogan R, Kim S, Chatr-Aryamontri A, Chang CS, Oughtred R, Rust J, Wilbur WJ, Comeau DC, Dolinski K, Tyers M . The BioC-BioGRID corpus: full text articles annotated for curation of protein-protein and genetic interactions . Database . 2017 . baw147 . 2017-01-01 . 28077563 . 5225395 . 10.1093/database/baw147 .
  52. Hirschman L, Yeh A, Blaschke C, Valencia A . Overview of BioCreAtIvE: critical assessment of information extraction for biology . BMC Bioinformatics . 6 . Suppl 1 . S1 . 2005 . 15960821 . 1869002 . 10.1186/1471-2105-6-S1-S1 . free .
  53. Krallinger M, Morgan A, Smith L, Leitner F, Tanabe L, Wilbur J, Hirschman L, Valencia A . Evaluation of text-mining systems for biology: overview of the Second BioCreative community challenge . En . Genome Biology . 9 . Suppl 2 . S1 . 2008 . 18834487 . 2559980 . 10.1186/gb-2008-9-s2-s1 . free .
  54. Li J, Sun Y, Johnson RJ, Sciaky D, Wei CH, Leaman R, Davis AP, Mattingly CJ, Wiegers TC, Lu Z . BioCreative V CDR task corpus: a resource for chemical disease relation extraction . Database . 2016 . baw068 . 2016 . 27161011 . 4860626 . 10.1093/database/baw068 .
  55. Pyysalo S, Ginter F, Heimonen J, Björne J, Boberg J, Järvinen J, Salakoski T . BioInfer: a corpus for information extraction in the biomedical domain . En . BMC Bioinformatics . 8 . 1 . 50 . February 2007 . 17291334 . 1808065 . 10.1186/1471-2105-8-50 . free .
  56. 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. S9. 10.1186/1471-2105-9-s11-s9. 2586758. 19025695 . free .
  57. Schwartz AS, Hearst MA . A simple algorithm for identifying abbreviation definitions in biomedical text . Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing . 451–62 . 2003 . 12603049 .
  58. Book: Rosario B, Hearst MA . 2005-10-06. Multi-way relation classification: application to protein-protein interactions. http://dl.acm.org/citation.cfm?id=1220575.1220667. Association for Computational Linguistics. 732–739. 10.3115/1220575.1220667. Hlt '05. Multi-way relation classification. 902226.
  59. Davis AP, Grondin CJ, Johnson RJ, Sciaky D, McMorran R, Wiegers J, Wiegers TC, Mattingly CJ . 6 . The Comparative Toxicogenomics Database: update 2019 . Nucleic Acids Research . 47 . D1 . D948–D954 . January 2019 . 30247620 . 6323936 . 10.1093/nar/gky868 .
  60. 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 . 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 . August 2012 . 22901054 . 3483229 . 10.1186/1471-2105-13-207 . free .
  61. 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 . 10.1093/bioinformatics/btg1023 . 12855455 . free .
  62. Web site: GENIA Project. www.geniaproject.org. 2018-10-06.
  63. Bachman JA, Gyori BM, Sorger PK . FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text mining . En . BMC Bioinformatics . 19 . 1 . 248 . June 2018 . 29954318 . 6022344 . 10.1186/s12859-018-2211-5 . free .
  64. 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 . 138–145 . BioNLP '06. 10.3115/1567619.1567652. free .
  65. 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.
  66. Medlock B, Briscoe T . 2007. Weakly Supervised Learning for Hedge Classification in Scientific Literature. Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics. 992–999.
  67. Book: Ding J, Berleant D, Nettleton D, Wurtele E . Mining MEDLINE: Abstracts, sentences, or phrases? . 2001 . Altman RB, Dunker AK, Hunter L, Lauderdale K, Klein TE . Pacific Symposium on Biocomputing 2002 . Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing . en . World Scientific . 326–337 . 10.1142/9789812799623_0031 . 11928487 . 9789810247775 . 10.1.1.385.6071 . registration . https://archive.org/details/pacificsymposium00paci/page/326.
  68. Kim J, Ohta T, Tsuruoka Y, Tateisi Y, Collier N . 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. 10.3115/1567594.1567610. free.
  69. Web site: LLLchallenge. genome.jouy.inra.fr. 2018-10-06.
  70. Web site: Medical Subject Headings - Home Page. www.nlm.nih.gov. 2018-10-06.
  71. Bodenreider O . The Unified Medical Language System (UMLS): integrating biomedical terminology . Nucleic Acids Research . 32 . Database issue . D267–70 . January 2004 . 14681409 . 308795 . 10.1093/nar/gkh061 .
  72. Web site: Metathesaurus . www.nlm.nih.gov . 2018-10-07.
  73. Johnson AE, Pollard TJ, Shen L, Lehman LW, Feng M, Ghassemi M, Moody B, Szolovits P, Celi LA, Mark RG . MIMIC-III, a freely accessible critical care database . En . Scientific Data . 3 . 160035 . May 2016 . 27219127 . 4878278 . 10.1038/sdata.2016.35 . 2016NatSD...360035J .
  74. Savova GK, Chapman WW, Zheng J, Crowley RS . Anaphoric relations in the clinical narrative: corpus creation . Journal of the American Medical Informatics Association . 18 . 4 . 459–65 . 2011 . 21459927 . 3128403 . 10.1136/amiajnl-2011-000108 .
  75. Book: Hersh W, Buckley C, Leone TJ, Hickam D . Sigir '94 . OHSUMED: An Interactive Retrieval Evaluation and New Large Test Collection for Research. 1994 . 192–201. Springer London. en. 10.1007/978-1-4471-2099-5_20. 9783540198895. 15094383 .
  76. Web site: Open Access Subset. www.ncbi.nlm.nih.gov. en. 2018-10-06.
  77. Nelson SJ, Zeng K, Kilbourne J, Powell T, Moore R . Normalized names for clinical drugs: RxNorm at 6 years . Journal of the American Medical Informatics Association . 18 . 4 . 441–8 . 2011 . 21515544 . 3128404 . 10.1136/amiajnl-2011-000116 .
  78. McCray AT . An upper-level ontology for the biomedical domain . Comparative and Functional Genomics . 4 . 1 . 80–4 . 2003 . 18629109 . 2447396 . 10.1002/cfg.255 .
  79. Web site: The UMLS Semantic Network. semanticnetwork.nlm.nih.gov. en. 2018-10-07.
  80. McCray AT, Srinivasan S, Browne AC . Lexical methods for managing variation in biomedical terminologies . Proceedings. Symposium on Computer Applications in Medical Care . 235–9 . 1994 . 7949926 . 2247735 .
  81. Web site: The SPECIALIST NLP Tools. lexsrv3.nlm.nih.gov. en. 2018-10-07.
  82. Jimeno-Yepes AJ, McInnes BT, Aronson AR . Exploiting MeSH indexing in MEDLINE to generate a data set for word sense disambiguation . En . BMC Bioinformatics . 12 . 1 . 223 . June 2011 . 21635749 . 3123611 . 10.1186/1471-2105-12-223 . free .
  83. Web site: Word Sense Disambiguation (WSD) Test Collections. wsd.nlm.nih.gov. 2018-10-07.
  84. Franzén K, Eriksson G, Olsson F, Asker L, Lidén P, Cöster J . Protein names and how to find them . International Journal of Medical Informatics . 67 . 1–3 . 49–61 . December 2002 . 12460631 . 10.1016/s1386-5056(02)00052-7 . 10.1.1.14.2183 .
  85. Mikolov T, Chen K, Corrado G, Dean J . 2013-01-16 . Efficient Estimation of Word Representations in Vector Space . 1301.3781 . cs.CL .
  86. Web site: BioASQ Releases Continuous Space Word Vectors Obtained by Applying Word2Vec to PubMed Abstracts bioasq.org. bioasq.org. 2018-11-07.
  87. Web site: bio.nlplab.org. bio.nlplab.org. 2018-11-07.
  88. Asgari E, Mofrad MR . Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics . PLOS ONE . 10 . 11 . e0141287 . 2015-11-10 . 26555596 . 4640716 . 10.1371/journal.pone.0141287 . 2015PLoSO..1041287A . 1503.05140 . free .
  89. Banerjee I, Madhavan S, Goldman RE, Rubin DL . Intelligent Word Embeddings of Free-Text Radiology Reports . AMIA ... Annual Symposium Proceedings. AMIA Symposium . 2017 . 411–420 . 2017 . 29854105 . 5977573 . 2017arXiv171106968B . 1711.06968 .
  90. Papanikolaou N, Pavlopoulos GA, Theodosiou T, Iliopoulos I . Protein-protein interaction predictions using text mining methods . Methods . 74 . 47–53 . March 2015 . 25448298 . 10.1016/j.ymeth.2014.10.026 .
  91. 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 . The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible . Nucleic Acids Research . 45 . D1 . D362–D368 . January 2017 . 27924014 . 5210637 . 10.1093/nar/gkw937 .
  92. Liem DA, Murali S, Sigdel D, Shi Y, Wang X, Shen J, Choi H, Caufield JH, Wang W, Ping P, Han J . 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 . October 2018 . 29775406 . 6230912 . 10.1152/ajpheart.00175.2018 .
  93. Yu S, Tranchevent LC, De Moor B, Moreau Y . Gene prioritization and clustering by multi-view text mining . BMC Bioinformatics . 11 . 1 . 28 . January 2010 . 20074336 . 3098068 . 10.1186/1471-2105-11-28 . free .
  94. Hu . Zhang-Zhi . Mani . Inderjeet . Hermoso . Vincent . Liu . Hongfang . Wu . Cathy H. . December 2004 . iProLINK: an integrated protein resource for literature mining . Computational Biology and Chemistry . en . 28 . 5–6 . 409–416 . 10.1016/j.compbiolchem.2004.09.010. 15556482 .
  95. Kankar P, Adak S, Sarkar A, Murari K, Sharma G . MedMeSH summarizer: text mining for gene clusters. . InProceedings of the 2002 SIAM International Conference on Data Mining . 11 April 2002 . 548–565 . Society for Industrial and Applied Mathematics . 10.1137/1.9781611972726.32 . 978-0-89871-517-0 . 10.1.1.215.6230 .
  96. Pyysalo S, Airola A, Heimonen J, Björne J, Ginter F, Salakoski T . Comparative analysis of five protein-protein interaction corpora . En . BMC Bioinformatics . 9 . Suppl 3 . S6 . April 2008 . 18426551 . 2349296 . 10.1186/1471-2105-9-s3-s6 . free .
  97. Kim S, Kwon D, Shin SY, Wilbur WJ . PIE the search: searching PubMed literature for protein interaction information . Bioinformatics . 28 . 4 . 597–8 . February 2012 . 22199390 . 3278758 . 10.1093/bioinformatics/btr702 .
  98. Gill N, Singh S, Aseri TC . Computational disease gene prioritization: an appraisal . Journal of Computational Biology . 21 . 6 . 456–465 . June 2014 . 24665902 . 10.1089/cmb.2013.0158 .
  99. Yu S, Van Vooren S, Tranchevent LC, De Moor B, Moreau Y . Comparison of vocabularies, representations and ranking algorithms for gene prioritization by text mining . Bioinformatics . 24 . 16 . i119–25 . August 2008 . 18689812 . 10.1093/bioinformatics/btn291 . free .
  100. Hulsegge I, Woelders H, Smits M, Schokker D, Jiang L, Sørensen P . Prioritization of candidate genes for cattle reproductive traits, based on protein-protein interactions, gene expression, and text-mining . Physiological Genomics . 45 . 10 . 400–6 . May 2013 . 23572538 . 10.1152/physiolgenomics.00172.2012 .
  101. Tao F, Zhuang H, Yu CW, Wang Q, Cassidy T, Kaplan LR, Voss CR, Han J . Multi-Dimensional, Phrase-Based Summarization in Text Cubes. . IEEE Data Eng. Bull. . 2016 . 39 . 3 . 74–84 .
  102. Thomas P, Starlinger J, Vowinkel A, Arzt S, Leser U . GeneView: a comprehensive semantic search engine for PubMed . Nucleic Acids Research . 40 . Web Server issue . W585–91 . July 2012 . 22693219 . 3394277 . 10.1093/nar/gks563 .
  103. Brown P, Zhou Y . Biomedical literature: Testers wanted for article search tool . Nature . 549 . 7670 . 31 . September 2017 . 28880292 . 10.1038/549031c . 2017Natur.549...31B . free .
  104. 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 . Finding useful data across multiple biomedical data repositories using DataMed . Nature Genetics . 49 . 6 . 816–819 . May 2017 . 28546571 . 6460922 . 10.1038/ng.3864 .
  105. Perez-Riverol Y, Bai M, da Veiga Leprevost F, Squizzato S, Park YM, Haug K, Carroll AJ, Spalding D, Paschall J, Wang M, Del-Toro N, Ternent T, Zhang P, Buso N, Bandeira N, Deutsch EW, Campbell DS, Beavis RC, Salek RM, Sarkans U, Petryszak R, Keays M, Fahy E, Sud M, Subramaniam S, Barbera A, Jiménez RC, Nesvizhskii AI, Sansone SA, Steinbeck C, Lopez R, Vizcaíno JA, Ping P, Hermjakob H . 6 . Discovering and linking public omics data sets using the Omics Discovery Index . Nature Biotechnology . 35 . 5 . 406–409 . May 2017 . 28486464 . 5831141 . 10.1038/nbt.3790 .
  106. Ide NC, Loane RF, Demner-Fushman D . Essie: a concept-based search engine for structured biomedical text . Journal of the American Medical Informatics Association . 14 . 3 . 253–63 . 2007-05-01 . 17329729 . 2244877 . 10.1197/jamia.m2233 .
  107. Lee HJ, Dang TC, Lee H, Park JC . OncoSearch: cancer gene search engine with literature evidence . Nucleic Acids Research . 42 . Web Server issue . W416–21 . July 2014 . 24813447 . 4086113 . 10.1093/nar/gku368 .
  108. Jenssen TK, Laegreid A, Komorowski J, Hovig E . 8889284 . A literature network of human genes for high-throughput analysis of gene expression . Nature Genetics . 28 . 1 . 21–8 . May 2001 . 11326270 . 10.1038/ng0501-21 .
  109. Masys DR . 52848745 . Linking microarray data to the literature . Nature Genetics . 28 . 1 . 9–10 . May 2001 . 11326264 . 10.1038/ng0501-9 .
  110. Doms A, Schroeder M . GoPubMed: exploring PubMed with the Gene Ontology . Nucleic Acids Research . 33 . Web Server issue . W783–6 . July 2005 . 15980585 . 1160231 . 10.1093/nar/gki470 .
  111. Turchin A, Florez Builes LF . Using Natural Language Processing to Measure and Improve Quality of Diabetes Care: A Systematic Review . Journal of Diabetes Science and Technology . 15 . 3 . 553–560 . May 2021 . 33736486 . 8120048 . 10.1177/19322968211000831 .
  112. Wang Y, Wang L, Rastegar-Mojarad M, Moon S, Shen F, Afzal N, Liu S, Zeng Y, Mehrabi S, Sohn S, Liu H . 6 . Clinical information extraction applications: A literature review . Journal of Biomedical Informatics . 77 . 34–49 . January 2018 . 29162496 . 5771858 . 10.1016/j.jbi.2017.11.011 .
  113. Friedman C . Towards a comprehensive medical language processing system: methods and issues . Proceedings . 595–599 . 1997 . 9357695 . 2233560 .
  114. Savova GK, Masanz JJ, Ogren PV, Zheng J, Sohn S, Kipper-Schuler KC, Chute CG . Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications . Journal of the American Medical Informatics Association . 17 . 5 . 507–513 . 2010 . 20819853 . 2995668 . 10.1136/jamia.2009.001560 .
  115. Soysal E, Wang J, Jiang M, Wu Y, Pakhomov S, Liu H, Xu H . CLAMP - a toolkit for efficiently building customized clinical natural language processing pipelines . Journal of the American Medical Informatics Association . 25 . 3 . 331–336 . March 2018 . 29186491 . 7378877 . 10.1093/jamia/ocx132 . free .
  116. Fries J, Wu S, Ratner A, Ré C . 2017-04-20 . SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data . 1704.06360 . cs.CL .
  117. Ye Z, Tafti AP, He KY, Wang K, He MM . SparkText: Biomedical Text Mining on Big Data Framework . PLOS ONE . 11 . 9 . e0162721 . 2016-09-29 . 27685652 . 5042555 . 10.1371/journal.pone.0162721 . 2016PLoSO..1162721Y . free .
  118. Tseytlin E, Mitchell K, Legowski E, Corrigan J, Chavan G, Jacobson RS . NOBLE - Flexible concept recognition for large-scale biomedical natural language processing . En . BMC Bioinformatics . 17 . 1 . 32 . January 2016 . 26763894 . 4712516 . 10.1186/s12859-015-0871-y . free .
  119. Web site: BioNLP - ACL Anthology. aclanthology.coli.uni-saarland.de. en. 2018-10-17.
  120. Web site: ISMB Proceedings. www.iscb.org. 2018-10-18.
  121. https://ieeexplore.ieee.org/xpl/conhome/1001586/all-proceedings
  122. Web site: dblp: CIKM. dblp.uni-trier.de. en. 2018-10-17.
  123. Web site: PSB Proceedings. psb.stanford.edu. 2018-10-18.
  124. Web site: dblp: Practical Applications of Computational Biology & Bioinformatics. dblp.org. en. 2018-10-17.
  125. Web site: Text REtrieval Conference (TREC) Proceedings. trec.nist.gov. 2018-10-17.

This article is licensed under the GNU Free Documentation License. It uses material from the Wikipedia article "Biomedical text mining".

Except where otherwise indicated, Everything.Explained.Today is © Copyright 2009-2024, A B Cryer, All Rights Reserved. Cookie policy.