Challenges in mining the literature for chemical information

Gurulingappa, Harsha; Mudi, Anirban; Toldo, Luca; Hofmann‐Apitius, Martin; Bhate, Jignesh

doi:10.1039/c3ra40787j

Cited by 26 publications

(30 citation statements)

References 83 publications

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“…Database curation standards are being constantly improved [105,106,107] to overcome the limitations associated with these tools [84] and make the tasks mentioned above easier. In the Era of Big Data, humans generate enormous amounts of information, which may be very difficult to find in either the literature [108] and databases. Users can compile resources from more than one database and directly from the literature to build datasets for further research.…”

Section: Application Of Databases In Analyses Of Datasets and Intementioning

confidence: 99%

Internet Databases of the Properties, Enzymatic Reactions, and Metabolism of Small Molecules—Search Options and Applications in Food Science

Minkiewicz

Darewicz

Iwaniak

et al. 2016

IJMS

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Internet databases of small molecules, their enzymatic reactions, and metabolism have emerged as useful tools in food science. Database searching is also introduced as part of chemistry or enzymology courses for food technology students. Such resources support the search for information about single compounds and facilitate the introduction of secondary analyses of large datasets. Information can be retrieved from databases by searching for the compound name or structure, annotating with the help of chemical codes or drawn using molecule editing software. Data mining options may be enhanced by navigating through a network of links and cross-links between databases. Exemplary databases reviewed in this article belong to two classes: tools concerning small molecules (including general and specialized databases annotating food components) and tools annotating enzymes and metabolism. Some problems associated with database application are also discussed. Data summarized in computer databases may be used for calculation of daily intake of bioactive compounds, prediction of metabolism of food components, and their biological activity as well as for prediction of interactions between food component and drugs.

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Section: Application Of Databases In Analyses Of Datasets and Intementioning

confidence: 99%

Internet Databases of the Properties, Enzymatic Reactions, and Metabolism of Small Molecules—Search Options and Applications in Food Science

Minkiewicz

Darewicz

Iwaniak

et al. 2016

IJMS

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show abstract

“…This approach presents a more objective perspective by extracting the data related information from publications in chemistry and has the potential to run in large scale and outline the landscape of data in a particular discipline. In chemistry, the data and information mining have been developed for both texts and graphics in publications, especially for chemical substance information, but manual curation is still needed to improve the automated mining results and the mining algorithms themselves [21].…”

Section: Understanding the Landscape Of Chemistry Data In Publicationsmentioning

confidence: 99%

Academic librarians at play in the field of cheminformatics: building the case for chemistry research data management

McEwen

2014

J Comput Aided Mol Des

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There are compelling needs from a variety of camps for more chemistry data to be available. While there are funder and government mandates for depositing research data in the United States and Europe, this does not mean it will be done well or expediently. Chemists themselves do not appear overly engaged at this stage and chemistry librarians who work directly with chemists and their local information environments are interested in helping with this challenge. Our unique understanding of organizing data and information enables us to contribute to building necessary infrastructure and establishing standards and best practices across the full research data cycle. As not many support structures focused on chemistry currently exist, we are initiating explorations through a few case studies and focused pilot projects presented here, with an aim of identifying opportunities for increased collaboration among chemists, chemistry librarians, cheminformaticians and other chemistry professionals.

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“…Examples include OSCAR4 which employs a maximum-entropy Markov model [ 3 ], and ChemSpot which employs a conditional random field model [ 4 ]. Comprehensive reviews of the area have been performed by Vazquez et al [ 5 ] and Gurulingappa et al [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

LeadMine: a grammar and dictionary driven approach to entity recognition

Lowe¹,

Sayle²

2015

J Cheminform

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BackgroundChemical entity recognition has traditionally been performed by machine learning approaches. Here we describe an approach using grammars and dictionaries. This approach has the advantage that the entities found can be directly related to a given grammar or dictionary, which allows the type of an entity to be known and, if an entity is misannotated, indicates which resource should be corrected. As recognition is driven by what is expected, if spelling errors occur, they can be corrected. Correcting such errors is highly useful when attempting to lookup an entity in a database or, in the case of chemical names, converting them to structures.ResultsOur system uses a mixture of expertly curated grammars and dictionaries, as well as dictionaries automatically derived from public resources. We show that the heuristics developed to filter our dictionary of trivial chemical names (from PubChem) yields a better performing dictionary than the previously published Jochem dictionary. Our final system performs post-processing steps to modify the boundaries of entities and to detect abbreviations. These steps are shown to significantly improve performance (2.6% and 4.0% F1-score respectively). Our complete system, with incremental post-BioCreative workshop improvements, achieves 89.9% precision and 85.4% recall (87.6% F1-score) on the CHEMDNER test set.ConclusionsGrammar and dictionary approaches can produce results at least as good as the current state of the art in machine learning approaches. While machine learning approaches are commonly thought of as "black box" systems, our approach directly links the output entities to the input dictionaries and grammars. Our approach also allows correction of errors in detected entities, which can assist with entity resolution.

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Challenges in mining the literature for chemical information

Cited by 26 publications

References 83 publications

Internet Databases of the Properties, Enzymatic Reactions, and Metabolism of Small Molecules—Search Options and Applications in Food Science

Internet Databases of the Properties, Enzymatic Reactions, and Metabolism of Small Molecules—Search Options and Applications in Food Science

Academic librarians at play in the field of cheminformatics: building the case for chemistry research data management

LeadMine: a grammar and dictionary driven approach to entity recognition

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