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Integrated Text Mining and Chemoinformatics Analysis Associates Diet to Health Benefit at Molecular Level

Overview of attention for article published in PLoS Computational Biology, January 2014
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Title
Integrated Text Mining and Chemoinformatics Analysis Associates Diet to Health Benefit at Molecular Level
Published in
PLoS Computational Biology, January 2014
DOI 10.1371/journal.pcbi.1003432
Pubmed ID
Authors

Kasper Jensen, Gianni Panagiotou, Irene Kouskoumvekaki

Abstract

Awareness that disease susceptibility is not only dependent on genetic make up, but can be affected by lifestyle decisions, has brought more attention to the role of diet. However, food is often treated as a black box, or the focus is limited to few, well-studied compounds, such as polyphenols, lipids and nutrients. In this work, we applied text mining and Naïve Bayes classification to assemble the knowledge space of food-phytochemical and food-disease associations, where we distinguish between disease prevention/amelioration and disease progression. We subsequently searched for frequently occurring phytochemical-disease pairs and we identified 20,654 phytochemicals from 16,102 plants associated to 1,592 human disease phenotypes. We selected colon cancer as a case study and analyzed our results in three directions; i) one stop legacy knowledge-shop for the effect of food on disease, ii) discovery of novel bioactive compounds with drug-like properties, and iii) discovery of novel health benefits from foods. This works represents a systematized approach to the association of food with health effect, and provides the phytochemical layer of information for nutritional systems biology research.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 116 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 2 2%
United States 2 2%
Spain 2 2%
Netherlands 1 <1%
Australia 1 <1%
Brazil 1 <1%
India 1 <1%
Korea, Republic of 1 <1%
United Kingdom 1 <1%
Other 3 3%
Unknown 101 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 32 28%
Student > Ph. D. Student 25 22%
Student > Master 12 10%
Student > Bachelor 10 9%
Student > Doctoral Student 6 5%
Other 13 11%
Unknown 18 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 22 19%
Computer Science 18 16%
Medicine and Dentistry 12 10%
Biochemistry, Genetics and Molecular Biology 11 9%
Pharmacology, Toxicology and Pharmaceutical Science 4 3%
Other 25 22%
Unknown 24 21%