University of Cambridge > > Machine Learning Journal Club > Predicting and understanding the stability of G-Quadruplexes

Predicting and understanding the stability of G-Quadruplexes

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G-quadruplexes are stable four-stranded guanine-rich structures which can form from DNA and RNA . They are an important component of human telomeres and play a role in the regulation of transcription and translation. The biological significance of a G-quadruplex is crucially linked with their thermodynamic stability. Hence the prediction of G-quadruplex stability is of vital interest. We present a novel Bayesian prediction framework based on Gaussian process regression to determine the thermodynamic stability of previously unmeasured G-quadruplex forming sequences from the sequence information alone. We benchmark our approach on a large G-quadruplex dataset and compare our method to standard approaches. Furthermore we propose an active learning procedure which can be used to iteratively acquire data in an optimal fashion. Lastly, we demonstrate the utility of our procedure on a genome-wide study of quadruplexes in the human genome.

This talk is part of the Machine Learning Journal Club series.

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