Computational identification of sequence variation and environmental condition in clinical depression from biomedical literature

Abstract

Clinical depression is a complex disease, which is known to be influenced by various factors. As genetic and environmental factors are frequently referred to as the most influential in causing depression, there have been many studies that try to identify genes or proteins and environmental conditions associated with depression. While a number of text-mining (TM) systems identifying information about the genetic factors in the biomedical literature have consequently been developed, there is currently no TM system specifically targeted at extracting environmental conditions. As a result, biologists are provided only with incomplete information about depression by these TM systems, unable to help them to discover the etiology and treatment of depression. In the thesis, we propose a TM system that considers an interaction between genetic and environmental factors associated with depression. The system identifies not only relations between a sequence variation and depression but also changes in the relations according to environmental conditions. In order to develop the system, we split the system into two TM subsystems. The first system is applied to an existing system for extracting the relations between a sequence variation and depression from the biomedical literature. The system classifies whether the relations are positive or negative on a document level. Based on the dictionary with candidate terms for environmental conditions, the second system identifies the conditions in the biomedical literature containing the binary relations. Using the dependency of sentence, the system excludes terms wrongly classified as the conditions. The system is a first TM system considering a ternary relation among sequence variation, disease and condition. Through the system, we are able to provide more comprehensive information about depression than other systems. We expect that, as the system is applied to other diseases, biologists can easily identify diverse information associated with changes in symptoms of diseases including depression.