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

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.人们逐渐认识到，疾病易感性不仅取决于遗传构成，还会受到生活方式选择的影响，这使得饮食的作用受到了更多关注。然而，食品常被视作黑箱，或是研究焦点仅局限于少量已被充分研究的化合物，例如多酚（polyphenols）、脂质（lipids）与营养素（nutrients）。本研究采用文本挖掘与朴素贝叶斯分类（Naïve Bayes classification）方法，构建了食品-植物化学物（phytochemical）与食品-疾病关联的知识空间，并在此框架中区分了疾病预防/缓解与疾病进展两类情况。随后，我们检索了高频出现的植物化学物-疾病关联对，并从16,102种植物中鉴定出20,654种植物化学物，这些物质与1,592种人类疾病表型相关。我们选取结肠癌作为案例研究对象，从三个方向对研究结果展开分析：i）面向食品对疾病影响的一站式既往知识库；ii）发现具有类药特性的新型生物活性化合物；iii）发掘食品的新型健康益处。本研究为食品与健康效应的关联分析提供了系统化方法，并为营养系统生物学（nutritional systems biology）研究提供了植物化学物层面的信息支撑。