Text mining for the biocuration workflow

Hirschman, Lynette; Burns, Gully A. P. C.; Krallinger, Martin; Arighi, Cecilia N.; Cohen, K. Bretonnel; Valencia, Alfonso; Wu, Cathy H.; Chatr‐aryamontri, Andrew; Dowell, Karen G.; Huala, Eva; Lourenço, Anália; Nash, Robert J.; Veuthey, Anne‐Lise; Wiegers, Thomas C.; Winter, Andrew G.

doi:10.1093/database/bas020

Cited by 137 publications

(124 citation statements)

References 21 publications

(49 reference statements)

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“…By providing formal semantics and ontology terms, computer reasoning can be enhanced and well-structured annotations produced in a more time-efficient manner. For instance, the combination of manual curation, as described here, with partially automated extraction of textual information [44][45][46] would dramatically speed up literature curation projects. We view these opportunities as interesting follow-up work to this study.…”

Section: Discussionmentioning

confidence: 99%

The KnownLeaf literature curation system captures knowledge about Arabidopsis leaf growth and development and facilitates integrated data mining

Szakonyi

Landeghem

Baerenfaller

et al. 2015

Current Plant Biology

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Please cite this article in press as: D. Szakonyi, et al., The KnownLeaf literature curation system captures knowledge about Arabidopsis leaf growth and development and facilitates integrated data mining, Curr. Plant Biol. (2015), http://dx. a b s t r a c tThe information that connects genotypes and phenotypes is essentially embedded in research articles written in natural language. To facilitate access to this knowledge, we constructed a framework for the curation of the scientific literature studying the molecular mechanisms that control leaf growth and development in Arabidopsis thaliana (Arabidopsis). Standard structured statements, called relations, were designed to capture diverse data types, including phenotypes and gene expression linked to genotype description, growth conditions, genetic and molecular interactions, and details about molecular entities. Relations were then annotated from the literature, defining the relevant terms according to standard biomedical ontologies. This curation process was supported by a dedicated graphical user interface, called Leaf Knowtator. A total of 283 primary research articles were curated by a community of annotators, yielding 9947 relations monitored for consistency and over 12,500 references to Arabidopsis genes. This information was converted into a relational database (KnownLeaf) and merged with other public Arabidopsis resources relative to transcriptional networks, protein-protein interaction, gene co-expression, and additional molecular annotations. Within KnownLeaf, leaf phenotype data can be searched together with molecular data originating either from this curation initiative or from external public resources. Finally, we built a network (LeafNet) with a portion of the KnownLeaf database content to graphically represent the leaf phenotype relations in a molecular context, offering an intuitive starting point for knowledge mining. Literature curation efforts such as ours provide high quality structured information accessible to computational analysis, and thereby to a wide range of applications.

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Section: Discussionmentioning

confidence: 99%

The KnownLeaf literature curation system captures knowledge about Arabidopsis leaf growth and development and facilitates integrated data mining

Szakonyi

Landeghem

Baerenfaller

et al. 2015

Current Plant Biology

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“…Michelson et al mined EMRs to detect surgical site infections (SSI) in unstructured clinical notes to improve SSI detection. 22 SSIs detected by traditional hospital-based surveillance were found using TM, along with an additional 37 SSIs not detected by traditional surveillance [122].…”

Section: Electronic Health Recordsmentioning

confidence: 97%

“…TM analysis typically involves a number of distinct phases, reviewed among others in [17,18,21,22], which are shown in Fig. 2 and described in detail below:…”

Section: Text Miningmentioning

confidence: 99%

Application of text mining in the biomedical domain

Fleuren

Alkema

2015

Methods

163

107

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“…Because the manual curation of the current exponentially growing body of biomedical literature is an impossible task, the insertion of robust text mining tools in the curation pipeline represent a feasible and sustainable solution to this problem (Hirschman, Burns et al 2012). …”

Section: Introductionmentioning

confidence: 99%

BioNLP 2017

Cohen¹,

Demner‐Fushman²,

Ananiadou³

et al. 2017

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According to the Association for Computational Linguistics guidelines on special interest groups (SIGs), The function of a SIG is to encourage interest and activity in specific areas within the ACL's field [1]. Is the SIGBioMed special interest group "within the ACL's field"? The titles of this year's papers suggest that it is, in that the current interest in deep learning in its many and varied manifestations is mirrored in those titles. Do those papers cover a specific area? They do, and in doing so, they demonstrate one of the great satisfactions of working in biomedical natural language processing.One of the joys of involvement in the biomedical natural language processing community is seeing the development of research with clinical applications. As examples of such work being presented at BioNLP 2017, we would like to point out the two papers that discuss the application of natural language processing to the diagnosis of neurological disorders. Bhatia et al. [2] describe an approach to using speech processing in the assessment of patients with amyotrophic lateral sclerosis (also known as Lou Gehrig's disease), one of the more horrific motor neuron diseases. Good assessment of amyotrophic lateral sclerosis patients is important for a number of reasons, including the fact that accurate tracking of the inevitable deterioration that is a hallmark of this disease gives patients and their families the possibility of purposeful planning for the attendant disability and death. However, current methodologies for evaluating the status of amyotrophic lateral sclerosis patients necessarily involve expensive equipment and highly trained personnel; when further developed, this methodology could make such evaluation much more, and more frequently, available to ALS patients. The fact that the work reported here involves a speech modality is especially exciting, as speech-related indicators of future ALS can be present long before diagnosis. The paper uses measurements of phonological features of speech and their divergence from a baseline, and demonstrates correlation with physiological measures.Adams et al. [3] describe work on detecting and categorizing word production errors associated with anomia, a particular kind of inability to find words. Screening for anomia is important because anomia is a symptom of stroke, but it is difficult and time-consuming to do, and therefore is not done as often as it should be. Automatic detection of anomia could be a nice enabler of improved care for stroke victims, but it is made difficult due to the subtlety of the phonological and semantic judgments that have to be made when assessing the phenomenon. The paper uses a combination of language modeling and phonologically-based edit distance calculation to approach the task, applying these techniques to data from the AphasiaBank collection of transcribed aphasic and healthy speech.Although we have summarized only these two examples that address neurological disorders, there are several other papers on the use of natural language proc...

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Text mining for the biocuration workflow

Cited by 137 publications

References 21 publications

The KnownLeaf literature curation system captures knowledge about Arabidopsis leaf growth and development and facilitates integrated data mining

The KnownLeaf literature curation system captures knowledge about Arabidopsis leaf growth and development and facilitates integrated data mining

Application of text mining in the biomedical domain

BioNLP 2017

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