Alessia Fenaroli: Propagating Quantitative Traits in Gene Networks

Adviser: Prof. Dr. M. Maathuis

February 2009

Abstract

Gene networks have been created to extend the knowledge of the gene functions in a specific organism. Such networks describe connections between genes involved in the same biological process.
McGary, Lee and Marcotte have related a gene network of the baker yeast, called the YeastNet, with a morphological traits variation dataset, the SCMD, and have defined a method which assigns scores to each gene of the network in order to predict their activity. The researchers have tested the predictability of YeastNet with ROC curves and the respective AUC values by computing a leave-one-out cross-validation and have obtained the median value 0.615.
Our contribution to this study includes: the definition of other score methods that take into account the quantitative data given by the SCMD dataset, in opposition to the dichotomization applied to these data made by McGary et all.; some new rules to predict the activity of each gene based on their scores, more complicated than the simple idea of comparing the scores with a cutoff adopted by McGary et all. but more efficient; and a different procedure, the 10-fold cross-validation, to compute the network predictability analysis.
Thanks to these changes we have improved the YeastNet prediction quality by 5%, whose median value now is 0.665.

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