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Open data science and algorithm development competitions offer a unique avenue for rapid discovery of better computational strategies. We highlight three examples in computational biology and bioinformatics research in which the use of competitions has yielded significant performance gains over established algorithms. These include algorithms for antibody clustering, imputing gene expression data, and querying the Connectivity Map (CMap). Performance gains are evaluated quantitatively using realistic, albeit sanitized, data sets. The solutions produced through these competitions are then examined with respect to their utility and the prospects for implementation in the field. We present the decision process and competition design considerations that lead to these successful outcomes as a model for researchers who want to use competitions and non-domain crowds as collaborators to further their research. [ABSTRACT FROM AUTHOR]

Increased serum uric acid (SUA) levels may be involved in the development of non-alcoholic fatty liver disease (NAFLD) in men presenting with metabolic syndrome (MetS) and/or insulin resistance. We aimed to determine the independent relationship between SUA and NAFLD in non-diabetic Chinese male population, and to explore the determinants of SUA levels among indexes of adiposity, lipid, and genotypes pertaining to triglycerides metabolism, inflammation, oxidative stress, and SUA concentrations. A total of 1440 men, classified depending on the presence of ultrasonographically detected NAFLD, underwent a complete healthy checkup program. Genotypes were extracted from our previously established genome-wide association study database. After adjusting for age, smoking, drinking, body mass index, homeostasis model assessment of insulin resistance, C-reactive protein, creatinine, alanine aminotransferase (ALT) and components of metabolic syndrome, the odds ratio for NAFLD, comparing the highest with the lowest SUA quartile, was 2.81 (95% confidence interval 1.66–4.76). A stepwise multivariate linear regression analysis (R2 = 0.238, P<0.001) retained age, waist circumference, serum creatinine, triglycerides, the Q141K variant in ABCG2 (rs2231142) and NAFLD as significant predictors of SUA levels (all P<0.001). Besides, ALT and Met196Arg variant in TNFRSF1B (rs1061622) additionally associated with SUA among individuls with NAFLD. Our data suggest that in Chinese men, elevated SUA is significantly associated with NAFLD, independent of insulin resistance and other metabolic disorders, such as central obesity or hypertriglyceridemia. Meanwhile, among subjects with NAFLD, index of liver damage, such as elevated ALT combined with genetic susceptibility to inflammation associated with increased SUA levels. [ABSTRACT FROM AUTHOR]