Bayesian regularization and pruning using a Laplace prior
Neural Computation
From promoter sequence to expression: a probabilistic framework
Proceedings of the sixth annual international conference on Computational biology
A discriminative model for identifying spatial cis-regulatory modules
RECOMB '04 Proceedings of the eighth annual international conference on Resaerch in computational molecular biology
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Peptide recognition modules (PRMs) are specialised compact protein domains that mediate many important protein-protein interactions. They are responsible for the assembly of critical macromolecular complexes and biochemical pathways [Pawson and Scott, 1997], and they have been implicated in carcinogenesis and various other human diseases [Sudol and Hunter, 2000]. PRMs recognise and bind to peptide ligands that contain a specific structural motif. This paper introduces a novel discriminative model which models these PRMs and allows prediction of their behaviour, which we compare with a recently proposed generative model. We find that on a yeast two-hybrid dataset, the generative model performs better when background sequences are included, while our discriminative model performs better when the evaluation is focused on discriminating between the SH3 domains. Our model is also evaluated on a phage display dataset, where it consistently out-performed the generative model.