Applications of Bayesian inference

GOR method

The GOR method (short for Garnier–Osguthorpe–Robson) is an information theory-based method for the prediction of secondary structures in proteins. It was developed in the late 1970s shortly after the simpler Chou–Fasman method. Like Chou–Fasman, the GOR method is based on probability parameters derived from empirical studies of known protein tertiary structures solved by X-ray crystallography. However, unlike Chou–Fasman, the GOR method takes into account not only the propensities of individual amino acids to form particular secondary structures, but also the conditional probability of the amino acid to form a secondary structure given that its immediate neighbors have already formed that structure. The method is therefore essentially Bayesian in its analysis. (Wikipedia).

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NOTE: This video has been updated. Please watch the new version: https://youtu.be/vP06aMoz4v8 In this StatQuest we talk about Sensitivity and Specificity - to key concepts for evaluating Machine Learning methods. These make it easier to choose which method is best for your data. NOTE: At

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Related pages

Amino acid | Alpha helix | Conditional probability | Information theory | Probability