Generating Hypotheses by Discovering Implicit Associations in the Literature: A Case Report of a Search for New Potential Therapeutic Uses for Thalidomide

Weeber, Marc; Vos, Rein; Hg, Klein; Berg, Lolkje Theodora Wilhelmina de Jong-van den; Aronson, Alan R.; Molema, Grietje

doi:10.1197/jamia.m1158

Cited by 167 publications

(80 citation statements)

References 43 publications

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“…Using such text-based inference, researchers have predicted new therapies for disease, novel applications of existing drugs, and connections between diseases. 5,[17][18][19][20] ChemoText was constructed by extracting the MeSH annotations from each article in MEDLINE, the database behind the National Library of Medicine's PubMed. The annotations were processed and organized into a database that allows known relationships between chemicals, proteins, and disease to be explored and new relationships to be inferred.…”

Section: Discussionmentioning

confidence: 99%

Possible Links between Sickle Cell Crisis and Pentavalent Antimony

Garcerant

Rubiano²,

Blanco³

et al. 2012

The American Society of Tropical Medicine and Hygiene

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Section: Discussionmentioning

confidence: 99%

Possible Links between Sickle Cell Crisis and Pentavalent Antimony

Garcerant

Rubiano²,

Blanco³

et al. 2012

The American Society of Tropical Medicine and Hygiene

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“…A lot of work has been done including concept term extraction [7] , association rules discovery [8,9] and extracting relationships between various concepts [4,6,[10][11][12] . Also, several natural Language processing (NLP) techniques were applied to biomedical documents, for example, in information extraction, extracting gene and protein interactions, named entity recognition (NER) [13] , protein-gene names disambiguation [3,14] and more.…”

Section: Related Workmentioning

confidence: 99%

“…Also, several natural Language processing (NLP) techniques were applied to biomedical documents, for example, in information extraction, extracting gene and protein interactions, named entity recognition (NER) [13] , protein-gene names disambiguation [3,14] and more. Although a lot of good research has been conducted for extracting important associations and interactions between various biological entities [2,[4][5][6][10][11][12][13]16] , discovering protein-disease associations, in particular, has not been investigated well in the literature. For example, Adamic et al [4] , has presented a statistical approach for discovering groups of genes related to a given disease.…”

Section: Related Workmentioning

confidence: 99%

A New Text Mining Approach for Finding Protein-to-Disease Associations

Al-Mubaid¹,

Singh²

2005

American J. of Biochemistry and Biotechnology

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Discovering significant relationships between biological entities from text documents is an important task for biologists in order to develop biological models for research and discovery, especially with the existing gigantic amounts of biomedical documents and the rate at which they are increasing everyday. We propose a new text mining method to extract associations between biological entities from text documents; and we focus and apply the method in our experiments on discovering proteins-to-diseases associations. The proposed method uses two sets of documents on the topic of interest [a negative set and positive (or relevant) set] and utilizes the concepts of expectation (ex), evidence (ev) and Z-scores in combining positive and negative evidences in determining the significant associations. Moreover, the method offers an efficient way to handle protein names, aliases and abbreviations and to disambiguate them from common abbreviations, gene symbols and such. We evaluated the method in discovering protein-to-disease associations from Medline abstracts and the results are very encouraging. We confirmed the correctness of the results, in each experiment, through articles from Medline. Our method was able to discover associations between certain proteins and various diseases like Alzheimer, Creutzfeldt-Jakob, Crohn Disease, Dengue, Jaundice, Lung cancer and more. For example, in Alzheimer test, the method ran on 83,933 abstracts and discovered that Alzheimer has significant association with 6 proteins, among them, Amyloid beta A4 protein precursor, Apolipoprotein E precursor and Presenilin 1 [PMIDs: 8596911, 1465129, 8346443, 12614323, 8766720 and 8878479]. We further tested our method on some already discovered and published relationships between genes and diseases and the method was also successful in supporting those discoveries.

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“…Finally, one of the most promising directions for further investigation is the extraction of semantic relations between records, 9 answering such questions as "What diseases does this drug treat?" or "What drugs treat this disease?"…”

mentioning

confidence: 99%

Exploring large document repositories with RDF technology: the DOPE project

et al. 2004

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Innovative research institutes rely on the availability of complete and accurate information about new research and development. Information providers such as Elsevier make it their business to provide the required information in a cost-effective way. The Semantic Web will likely contribute significantly to this effort because it facilitates access to an unprecedented quantity of data. The DOPE project (Drug Ontology Project for Elsevier) explores ways to provide access to multiple lifescience information sources through a single interface.With the unremitting growth of scientific information, integrating access to all this information remains an important problem, primarily because the information sources involved are so heterogeneous. Sources might use different syntactic standards (syntactic heterogeneity), organize information in different ways (structural heterogeneity), and even use different terminologies to refer to the same information (semantic heterogeneity). Integrated access hinges on the ability to address these different kinds of heterogeneity.Also, mental models and keywords for accessing data generally diverge between subject areas and communities; hence, many different ontologies have emerged. An ideal architecture must therefore support the disclosure of distributed and heterogeneous data sources through different ontologies. To serve this need, we've developed a thesaurus-based search system that uses automatic indexing, RDF-based querying, and concept-based visualization. We describe here the conversion of an existing proprietary thesaurus to an open standard format, a generic architecture for thesaurus-based information access, an innovative user interface, and results of initial user studies with the resulting DOPE system. Thesaurus-based information accessThesauri have proven to be essential for effective information access. They provide controlled vocabularies for indexing information and thereby help to overcome many free-text search problems by relating and grouping relevant terms in a specific domain. Thesauri in the life sciences include MeSH, produced by the US National Library of Medicine (www. nlm.nih.gov/mesh/meshhome.html) and EMTREE, Elsevier's life science thesaurus (www.elsevier.com/ homepage/sah/spd/site).These thesauri provide access to information sources (in particular document repositories) such as PubMed (http://pubmed.org) and EMBASE.com (http://embase.com), but currently no open architecture exists to support using these thesauri for querying other data sources. For example, when we move from centralized, controlled use of EMTREE within EMBASE.com to a distributed setting, we must improve access to the thesaurus with a standardized representation using open data standards that allow for semantic qualifications. RDF (Resource Description Framework) is such a standard.Elsevier maintains the EMTREE thesaurus as a termi-

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Generating Hypotheses by Discovering Implicit Associations in the Literature: A Case Report of a Search for New Potential Therapeutic Uses for Thalidomide

Cited by 167 publications

References 43 publications

Possible Links between Sickle Cell Crisis and Pentavalent Antimony

Possible Links between Sickle Cell Crisis and Pentavalent Antimony

A New Text Mining Approach for Finding Protein-to-Disease Associations

Exploring large document repositories with RDF technology: the DOPE project

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