Discovery and assessment of gene-disease associations by integrated analysis of scientific literature and microarray data

Faro, Alberto; Giordano, Daniela; Spampinato, Concetto

doi:10.1109/itab.2010.5687757

Cited by 2 publications

(1 citation statement)

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“…In fact by only using the terms of standard vocabularies (e.g. Entrez Gene, UniProt) it may happen that no association is derived because of the dissimilarities between the vocabularies' terms and the terms extracted from parsing full papers and abstracts; 3) Document clustering using co-occurrences [15], [16] of dictionaries' terms in the documents to build relationships gene-disease, gene-gene, protein-protein, proteindisease. Fig.…”

Section: Biowizardmentioning

confidence: 99%

BioWizard: Discovering and validating associations between biological entities by integrated analysis of scientific literature and experimental data

Spampinato

Giordano

Kavasidis

et al. 2012

2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)

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In this paper, we present BioWizard, a bioinformatics knowledge discovery tool for extracting and validating implicit associations between biological entities. By mining specialized scientific literature, BioWizard not only generates biological hypotheses in the form of associations between genes, proteins and diseases, but also validates the plausibility of such associations against high-throughput biological data (microarrays) and annotated databases. The main novelties of the proposed approach are that: 1) it infers associations between biological entities by mining full text papers instead of only abstracts as usually performed by the existing tools, 2) a named entity recognition that improves the precision of the derived associations by enriching the vocabularies used in the mining loop with terms extracted directly from the text and, 3) the inferred associations are filtered according to their evidence in experimental data. We tested the precision and the recall of our system in retrieving known-associations (which did not appear in the same document) from gold standards and the results shown the ability of BioWizard in retrieving valid associations, thus providing a valuable tool for the use of biomedical researchers to speed up scientific progress.

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Section: Biowizardmentioning

confidence: 99%

BioWizard: Discovering and validating associations between biological entities by integrated analysis of scientific literature and experimental data

Spampinato

Giordano

Kavasidis

et al. 2012

2012 25th IEEE International Symposium on Computer-Based Medical Systems (CBMS)

Self Cite

View full text Add to dashboard Cite

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Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends

et al. 2016

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BackgroundBreast cancer is a serious disease which affects many women and may lead to death. It has received considerable attention from the research community. Thus, biomedical researchers aim to find genetic biomarkers indicative of the disease. Novel biomarkers can be elucidated from the existing literature. However, the vast amount of scientific publications on breast cancer make this a daunting task. This paper presents a framework which investigates existing literature data for informative discoveries. It integrates text mining and social network analysis in order to identify new potential biomarkers for breast cancer.ResultsWe utilized PubMed for the testing. We investigated gene–gene interactions, as well as novel interactions such as gene-year, gene-country, and abstract-country to find out how the discoveries varied over time and how overlapping/diverse are the discoveries and the interest of various research groups in different countries.ConclusionsInteresting trends have been identified and discussed, e.g., different genes are highlighted in relationship to different countries though the various genes were found to share functionality. Some text analysis based results have been validated against results from other tools that predict gene–gene relations and gene functions.Electronic supplementary materialThe online version of this article (doi:10.1186/s13104-016-2023-5) contains supplementary material, which is available to authorized users.

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Discovery and assessment of gene-disease associations by integrated analysis of scientific literature and microarray data

Cited by 2 publications

References 18 publications

BioWizard: Discovering and validating associations between biological entities by integrated analysis of scientific literature and experimental data

BioWizard: Discovering and validating associations between biological entities by integrated analysis of scientific literature and experimental data

Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends

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