publication . Article . 2014

Integrated text mining and chemoinformatics analysis associates diet to health benefit at molecular level.

Kasper P. Jensen; Gianni Panagiotou; Irene Kouskoumvekaki;
Open Access English
  • Published: 16 Jan 2014 Journal: PLoS Computational Biology, volume 10, issue 1 (issn: 1553-7358, Copyright policy)
  • Publisher: Public Library of Science (PLoS)
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.
Author Summary Until recently diet was considered a supplier of energy and building blocks for growth and development. However, current research in the field suggests that the complex mixture of natural compounds present in our food has a variety of biological activities and plays an important role for health maintenance and disease prevention. The mixture of bioactive components of our diet interacts with the human body through complex processes that modify network function and stability. In order to increase our limited understanding on how components of food affect human health, we borrow methods that are well established in medical and pharmacological research. By using text mining in PubMed abstracts we collected more than 20,000 diverse chemical structures present in our diet, while by applying chemoinformatics methods we could systematically explore their numerous targets. Integrating the above datasets with food-disease associations allowed us to use a statistical framework for identifying specific phytochemicals as perturbators of drug targets and disease related pathways.
Persistent Identifiers
Sustainable Development Goals (SDG)
6. Clean water
free text keywords: Research Article, Biology, Systems Biology, Chemistry, Computational Chemistry, Medicinal Chemistry, Phytochemistry, Computer Science, Natural Language Processing, Text Mining, Medicine, Nutrition, Computational Theory and Mathematics, Cellular and Molecular Neuroscience, Genetics, Molecular Biology, Ecology, Modelling and Simulation, Ecology, Evolution, Behavior and Systematics, DIET, Physiological aspects, /dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being, SDG 3 - Good Health and Well-being, lcsh:Biology (General), lcsh:QH301-705.5, Disease, Systems biology, Cheminformatics, Phytochemical, Text mining, business.industry, business, Biology, Health informatics, Health effect, Bioinformatics, Computational biology, MEDLINE
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