Pharmspresso: a text mining tool for extraction of pharmacogenomic concepts and relationships from full text

Abstract Background Blood cancers (BCs) are responsible for over 720 K yearly deaths worldwide. Their prevalence and mortality-rate uphold the relevance of research related to BCs. Despite the availability of different resources establishing Disease-Disease Associations (DDAs), the knowledge is scattered and not accessible in a straightforward way to the scientific community. Here, we propose SicknessMiner, a biomedical Text-Mining (TM) approach towards the centralization of DDAs. Our methodology encompasses Named Entity Recognition (NER) and Named Entity Normalization (NEN) steps, and the DDAs retrieved were compared to the DisGeNET resource for qualitative and quantitative comparison. Results We obtained the DDAs via co-mention using our SicknessMiner or gene- or variant-disease similarity on DisGeNET. SicknessMiner was able to retrieve around 92% of the DisGeNET results and nearly 15% of the SicknessMiner results were specific to our pipeline. Conclusions SicknessMiner is a valuable tool to extract disease-disease relationship from RAW input corpus.

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Text mining tool for translating terms of contract into technical specifications: Development and application in the railway sector

Computers in Industry ◽

10.1016/j.compind.2020.103357 ◽

2021 ◽

Vol 124 ◽

pp. 103357

Author(s):

G. Fantoni ◽

E. Coli ◽

F. Chiarello ◽

R. Apreda ◽

F. Dell’Orletta ◽

...

Keyword(s):

Text Mining ◽

Technical Specifications ◽

Mining Tool ◽

Text Mining Tool

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Testing a text mining tool for emerging risk identification

EFSA Supporting Publications ◽

10.2903/sp.efsa.2016.en-1154 ◽

2016 ◽

Vol 13 (12) ◽

Cited By ~ 7

Author(s):

Niels B. Lucas Luijckx ◽

Fred J. van de Brug ◽

Winfried R. Leeman ◽

Jos M.B.M. van der Vossen ◽

Hilde J. Cnossen

Keyword(s):

Text Mining ◽

Risk Identification ◽

Emerging Risk ◽

Mining Tool ◽

Text Mining Tool

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Extracting kinetic information from literature with KineticRE

Journal of Integrative Bioinformatics ◽

10.1515/jib-2015-282 ◽

2015 ◽

Vol 12 (4) ◽

pp. 56-68

Author(s):

Ana Alão Freitas ◽

Hugo Costa ◽

Isabel Rocha

Keyword(s):

Text Mining ◽

Metabolic Networks ◽

Scientific Literature ◽

Kluyveromyces Lactis ◽

Relevant Information ◽

Text Documents ◽

Kinetic Information ◽

Mining Tool ◽

Text Mining Tool

Summary To better understand the dynamic behavior of metabolic networks in a wide variety of conditions, the field of Systems Biology has increased its interest in the use of kinetic models. The different databases, available these days, do not contain enough data regarding this topic. Given that a significant part of the relevant information for the development of such models is still wide spread in the literature, it becomes essential to develop specific and powerful text mining tools to collect these data. In this context, this work has as main objective the development of a text mining tool to extract, from scientific literature, kinetic parameters, their respective values and their relations with enzymes and metabolites. The approach proposed integrates the development of a novel plug-in over the text mining framework @Note2. In the end, the pipeline developed was validated with a case study on Kluyveromyces lactis, spanning the analysis and results of 20 full text documents.

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BioReader: a text mining tool for performing classification of biomedical literature

BMC Bioinformatics ◽

10.1186/s12859-019-2607-x ◽

2019 ◽

Vol 19 (S13) ◽

Cited By ~ 9

Author(s):

Christian Simon ◽

Kristian Davidsen ◽

Christina Hansen ◽

Emily Seymour ◽

Mike Bogetofte Barnkob ◽

...

Keyword(s):

Text Mining ◽

Biomedical Literature ◽

Mining Tool ◽

Text Mining Tool

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TOPIC MODELING IN CLINICAL REPORTS - A SURVEY

International Journal of Advanced Information and Communication Technology ◽

10.46532/ijaict-2020002 ◽

2020 ◽

pp. 6-10

Author(s):

Ponmalar R ◽

Ponnarasi D ◽

Sangeetha A ◽

Kingsy Grace R

Keyword(s):

Text Mining ◽

Topic Modeling ◽

Unstructured Data ◽

Modeling Techniques ◽

Mining Tool ◽

Text Mining Tool ◽

Extract Information ◽

Meaningful Data

Text mining is a process of converting unstructured data into meaningful data. It may be loosely characterized as the process of analyzing text to extract information that is useful for particular purposes. Topic modeling is a form of text mining, a way of identifying patterns in a corpus. The topics produced by topic modeling techniques are clusters of similar words that are frequently occur together. Topic modeling is also a frequently used text-mining tool for discovery of hidden semantic structures in a text body. Intuitively, a document is about a particular topic, one would expect particular words to appear in the document more or less frequently. This paper, presents a survey on topic modeling in clinical documents.

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MACE2K: A Text-Mining Tool to Extract Literature-based Evidence for Variant Interpretation using Machine Learning

10.1101/2020.12.03.409094 ◽

2020 ◽

Author(s):

Samir Gupta ◽

Shruti Rao ◽

Trisha Miglani ◽

Yasaswini Iyer ◽

Junxia Lin ◽

...

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Text Mining ◽

Genomic Medicine ◽

Relevant Information ◽

Biomedical Literature ◽

Variant Interpretation ◽

Learning Models ◽

Mining Tool ◽

Text Mining Tool

AbstractInterpretation of a given variant’s pathogenicity is one of the most profound challenges to realizing the promise of genomic medicine. A large amount of information about associations between variants and diseases used by curators and researchers for interpreting variant pathogenicity is buried in biomedical literature. The development of text-mining tools that can extract relevant information from the literature will speed up and assist the variant interpretation curation process. In this work, we present a text-mining tool, MACE2k that extracts evidence sentences containing associations between variants and diseases from full-length PMC Open Access articles. We use different machine learning models (classical and deep learning) to identify evidence sentences with variant-disease associations. Evaluation shows promising results with the best F1-score of 82.9% and AUC-ROC of 73.9%. Classical ML models had a better recall (96.6% for Random Forest) compared to deep learning models. The deep learning model, Convolutional Neural Network had the best precision (75.6%), which is essential for any curation task.

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