Title |
Integrated Text Mining and Chemoinformatics Analysis Associates Diet to Health Benefit at Molecular Level
|
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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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 8 | 50% |
Germany | 1 | 6% |
Switzerland | 1 | 6% |
United Kingdom | 1 | 6% |
Unknown | 5 | 31% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 11 | 69% |
Scientists | 4 | 25% |
Practitioners (doctors, other healthcare professionals) | 1 | 6% |
Mendeley readers
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% |