ArticleBMC bioinformatics2010
Latent physiological factors of complex human diseases revealed by independent component analysis of clinarrays.
Article in BMC bioinformatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
- Identifying and mitigating biases in EHR laboratory tests.Journal of biomedical informatics · 2014Article
- Defining a comprehensive verotype using electronic health records for personalized medicine.Journal of the American Medical Informatics Association : JAMIA · 2013Article
- Exploiting time in electronic health record correlations.Journal of the American Medical Informatics Association : JAMIA · 2011Article
- Selected proceedings of the 2010 Summit on Translational Bioinformatics.BMC bioinformatics · 2010Article
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3 authors at 1 institution in 1 country.
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Abstract
backgroundDiagnosis and treatment of patients in the clinical setting is often driven by known symptomatic factors that distinguish one particular condition from another. Treatment based on noticeable symptoms, however, is limited to the types of clinical biomarkers collected, and is prone to overlooking dysfunctions in physiological factors not easily evident to medical practitioners. We used a vector-based representation of patient clinical biomarkers, or clinarrays, to search for latent physiological factors that underlie human diseases directly from clinical laboratory data. Knowledge of these factors could be used to improve assessment of disease severity and help to refine strategies for diagnosis and monitoring disease progression.
resultsApplying Independent Component Analysis on clinarrays built from patient laboratory measurements revealed both known and novel concomitant physiological factors for asthma, types 1 and 2 diabetes, cystic fibrosis, and Duchenne muscular dystrophy. Serum sodium was found to be the most significant factor for both type 1 and type 2 diabetes, and was also significant in asthma. TSH3, a measure of thyroid function, and blood urea nitrogen, indicative of kidney function, were factors unique to type 1 diabetes respective to type 2 diabetes. Platelet count was significant across all the diseases analyzed.
conclusionsThe results demonstrate that large-scale analyses of clinical biomarkers using unsupervised methods can offer novel insights into the pathophysiological basis of human disease, and suggest novel clinical utility of established laboratory measurements.
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