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Abstract:
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The ability to predict is one of the hallmarks of successful theories . Historically , the predictive power of biology has lagged behind disciplines like physics because the biological world is complex , challenging to quantify , and full of exceptions . However , in recent years the amount of available data has expanded exponentially and biological predictions based on this data become a possibility . The functional gene network is a quantitative way to integrate this data and a useful framework for making biological predictions . This study demonstrates that functional networks capture real biological insight and uses the network to predict both subcellular protein localization and the phenotypic outcome of gene knockouts . Furthermore , I use the functional network to evaluate genetic modules shared between diverse organisms that lead to orthologous phenotypes , many that are non -obvious . I show that the successful predictions of the functional network have broad applicability and implications that range from the design of large -scale biological experiments to the discovery of genes with potential roles in human disease . |