Machine Learning Methods for Protein Structure Prediction

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Deep Supervised and Convolutional Generative Stochastic Network for Protein Secondary Structure Prediction (2014, background): “Understanding complex dependency between protein sequence and structure is one of the greatest challenges in computational biology (Cheng et al., 2008).”
Improving protein fold recognition by random forest (2014, methodology): “Machine learning methods have been used to tackle the problem effectively by casting the fold recognition as a binary classification problem to decide whether or not a target protein shares the same structural fold with a template protein in a protein structure library [6-8].”