A DYNAMIC BAYESIAN NETWORK PREDICTOR OF PROTEIN SECONDARY STRUCTURE

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. This work focuses on the prediction of protein secondary structure, which is the locally ordered structure that is created by hydrogen bonding within the protein backbone. In this project, we solve this problem using a class of probability models known as dynamic Bayesian networks. Specifically, we aim to investigate whether approaches that provide sparse models will allow us to build better models with fewer parameters so that we can give priority to biologically significant correlations and eliminate the ones that are irrelevant.