The functional network in predictive biology : predicting phenotype from genotype and predicting human disease from fungal phenotype
MetadataShow full item record
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.
Showing items related by title, author, creator and subject.
Prediction and verification of microRNA targets by MovingTargets, a highly adaptable prediction method Burgler, Craig; Macdonald, Paul M. (BMC Genomics, 2005-06-08)Background: MicroRNAs (miRNAs) mediate a form of translational regulation in animals. Hundreds of animal miRNAs have been identified, but only a few of their targets are known. Prediction of miRNA targets for translational ...
Farooq, Muhammad Umar, active 2013 (2013-12)Performance of modern pipelined processor depends on steady flow of useful instructions for processing. Branch instruction disrupts sequential flow of instructions by presenting multiple paths through which a program can ...
The "Science" of Prediction: A Battery of Blind Back-Dated Validation Tests of PredictionWorks' New Venture Assessment System Butler, John Sibley; Cadenhead, Gary M. (McCombs School of Business, The University of Texas at AustinIC² Institute, The University of Texas at Austin, 2016-11-01)This report examines the results of a research partnership between PredictionWorks, a private firm, and The University of Texas at Austin (McCombs School of Business and the IC² Institute). PredictionWorks' New Venture ...