On Building a Quantitative Food-Disease-Gene Network

Nutritional genomics is a new science that studies the relationship between foods (or nutrients), diseases, and genes. Large amounts of scientific findings have been published in this area, primarily in unstructured text. Moreover, given a pair of entities, different studies can report different findings. It is hence important to obtain a holistic view of the reported relationships. In this article, we describe an information extraction system aiming to reach this goal. The system integrates natural language processing techniques, domain ontology, statistical, and machine learning methods. It consists of four main modules: (1) entity extraction, which recognizes and extracts five types of entities: foods, chemicals (or nutrients), diseases, proteins and genes; (2) relationship extraction, which extracts binary relationships between entities; (3) relationship polarity analysis, which categorizes relationships into three groups: positive, negative, and neutral; and (4) strength analysis, which rates a relationship as weak, medium, or strong. To the best of our knowledge, we are the first to propose to analyze the polarity and strength of a binary relationship. We have evaluated our system using the GENIA corpus and datasets drawn from the MEDLINE database. The first two modules outperform the reported best results with an average F-score of 0.89 and 0.82, respectively; while the last two also achieve promising results with an accuracy of 0.75-0.84 and ~0.90, respectively. 1 INTRODUCTION Advances in biotechnology and life sciences are leading to an ever-increasing volume of published research data, predominantly in unstructured text (or natural language). At the time of writing, the MEDLINE database consists of 19 million scientific articles with a growth rate of ~400,000 articles per year [8]. This phenomenon becomes even more apparent in nutritional genomics, an emerging new science that studies the relationship between foods (or nutrients), diseases, and genes [16]. For instance, soy products and green tea have been two of the intensively studied foods in this new discipline due to their controversial relationship with cancer. A search to the MEDLINE database on " soy and cancer " renders a total of 1,287 articles, and a search on " green tea and cancer " renders 1,318 articles. Due to the large number of publications every year, it is unrealistic for even the most motivated to manually go through these articles to obtain a full picture of the findings reported to date. This however has become ever more important and necessary due to the following reasons: (1) given a …