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BMC Bioinformatics

, 18:340

Networks analysis


BackgroundClinical signs are a fundamental aspect of human pathologies. While disease diagnosis is problematic or impossible in many cases, signs are easier to perceive and categorize. Clinical signs are increasingly used, together with molecular networks, to prioritize detected variants in clinical genomics pipelines, even if the patient is still undiagnosed. Here we analyze the ability of these network-based methods to predict genes that underlie clinical signs from the human interactome.

ResultsOur analysis reveals that these approaches can locate genes associated with clinical signs with variable performance that depends on the sign and associated disease. We analyzed several clinical and biological factors that explain these variable results, including number of genes involved mono- vs. oligogenic diseases, mode of inheritance, type of clinical sign and gene product function.

ConclusionsOur results indicate that the characteristics of the clinical signs and their related diseases should be considered for interpreting the results of network-prediction methods, such as those aimed at discovering disease-related genes and variants. These results are important due the increasing use of clinical signs as an alternative to diseases for studying the molecular basis of human pathologies.

KeywordsGene prioritization Human interactome Clinical signs Network-based methods Electronic supplementary materialThe online version of this article doi:10.1186-s12859-017-1754-1 contains supplementary material, which is available to authorized users.

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Autor: Sara González-Pérez - Florencio Pazos - Mónica Chagoyen

Fuente: https://link.springer.com/

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