Abstract
BackgroundRecently, research on human disease network has succeeded and has become an aid in figuring out the relationship between various diseases. In most disease networks, however, the relationship between diseases has been simply represented as an association. This representation results in the difficulty of identifying prior diseases and their influence on posterior diseases. In this paper, we propose a causal disease network that implements disease causality through text mining on biomedical literature.MethodsTo identify the causality between diseases, the proposed method includes two schemes: the first is the lexicon-based causality term strength, which provides the causal strength on a variety of causality terms based on lexicon analysis. The second is the frequency-based causality strength, which determines the direction and strength of causality based on document and clause frequencies in the literature.ResultsWe applied the proposed method to 6,617,833 PubMed literature, and chose 195 diseases to construct a causal disease network. From all possible pairs of disease nodes in the network, 1011 causal pairs of 149 diseases were extracted. The resulting network was compared with that of a previous study. In terms of both coverage and quality, the proposed method showed outperforming results; it determined 2.7 times more causalities and showed higher correlation with associated diseases than the existing method.ConclusionsThis research has novelty in which the proposed method circumvents the limitations of time and cost in applying all possible causalities in biological experiments and it is a more advanced text mining technique by defining the concepts of causality term strength.
Highlights
Research on human disease network has succeeded and has become an aid in figuring out the relationship between various diseases
In the proposed method, we define the concepts of causality term strength based on lexical semantics and define the causality frequency based on biomedical literature to discover the causal relationships between diseases, along with their strength and directions
Results of causal disease network construction To demonstrate how the lexicon-based causality term strength and the frequency-based causality strength are applied to 195 selected diseases, we consider an exemplary case of “Hepatitis C and Hepatocellular Carcinoma” and explain the process using them
Summary
Research on human disease network has succeeded and has become an aid in figuring out the relationship between various diseases. The relationship between diseases has been represented as an association. Research on human diseases has been a major issue in biology and medical fields. Research activities on these subjects were carried out based on genetic, biological, and epidemiological information [1,2,3] in the past, and success in multi-omics approaches has shed light on recent researches on human disease network. The limitation was mainly due to lack of information that determines causal relationship between diseases. If we can determine the causal relationship of these diseases, we can apply priority prevention of posterior disease and choose an appropriate treatment method. Bang et al [14] proposed a causality modeling by using various biomedical data including gene/protein, clinical, metabolic pathway information to construct a disease causality network
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