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Open Access Highly Accessed Methodology article

Molecular mechanistic associations of human diseases

Philip Stegmaier1*, Mathias Krull1, Nico Voss1, Alexander E Kel12 and Edgar Wingender13

Author Affiliations

1 BIOBASE GmbH, Halchtersche Strasse 33, D-38304 Wolfenbüttel, Germany

2 Institute of Chemical Biology and Fundamental Medicine, Lavrentiev Ave.8, 630090, Novosibirsk, Russia

3 Department of Bioinformatics, Medical School, Georg August University Göttingen, Goldschmidtstrasse 1, D-37077 Göttingen, Germany

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BMC Systems Biology 2010, 4:124  doi:10.1186/1752-0509-4-124

Published: 6 September 2010

Abstract

Background

The study of relationships between human diseases provides new possibilities for biomedical research. Recent achievements on human genetic diseases have stimulated interest to derive methods to identify disease associations in order to gain further insight into the network of human diseases and to predict disease genes.

Results

Using about 10000 manually collected causal disease/gene associations, we developed a statistical approach to infer meaningful associations between human morbidities. The derived method clustered cardiometabolic and endocrine disorders, immune system-related diseases, solid tissue neoplasms and neurodegenerative pathologies into prominent disease groups. Analysis of biological functions confirmed characteristic features of corresponding disease clusters. Inference of disease associations was further employed as a starting point for prediction of disease genes. Efforts were made to underpin the validity of results by relevant literature evidence. Interestingly, many inferred disease relationships correspond to known clinical associations and comorbidities, and several predicted disease genes were subjects of therapeutic target research.

Conclusions

Causal molecular mechanisms present a unifying principle to derive methods for disease classification, analysis of clinical disorder associations, and prediction of disease genes. According to the definition of causal disease genes applied in this study, these results are not restricted to genetic disease/gene relationships. This may be particularly useful for the study of long-term or chronic illnesses, where pathological derangement due to environmental or as part of sequel conditions is of importance and may not be fully explained by genetic background.