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Gene regulation at the posttranscriptional level is often mediated by trans-acting factors binding the 3′ untranslated region (3′ UTR) of messenger RNAs (mRNAs). Alternative mRNA isoforms differing only in their 3′ UTR can thus be differentially regulated, and it has been recently shown that this mechanism is indeed used by the cell to alter gene regulation effected by microRNAs and RNA-binding proteins, especially in highly proliferating contexts. Here we describe a computational method to analyze alternative 3′ UTR isoforms in gene expression profiling datasets obtained with Affymetrix 3′ IVT microarrays. The approach we describe allows the analysis of 3′ UTR isoform usage in thousands of publicly available gene expression datasets, including many retrospective studies of cancer patients equipped with clinical data. [ABSTRACT FROM AUTHOR]

The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many ˵gene expression signatures″ have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established 70-gene signature. We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptron and Random Forest in classifying patients from the NKI breast cancer dataset, and slightly better than the scoring-based method originally proposed by the authors of the seventy-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection. Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data. [ABSTRACT FROM AUTHOR]

String-like states appear naturally in the spectrum of two-dimensional Yang-Mills theory (YM2) on a torus, quantized in the gauge where the field strength is diagonal. These states are completely analogous to the ones appearing in Matrix String theory, and originate from topological obstructions to a global smooth diagonalization. [ABSTRACT FROM AUTHOR]