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Shapovalov M, Dunbrack RL, Vucetic S
Multifaceted analysis of training and testing convolutional neural networks for protein secondary structure prediction
PLoS One (2020) 15:e0232528.
Protein secondary structure prediction remains a vital topic with broad applications. Due to lack of a widely accepted standard in secondary structure predictor evaluation, a fair comparison of predictors is challenging. A detailed examination of factors that contribute to higher accuracy is also lacking. In this paper, we present: (1) new test sets, Test2018, Test2019, and Test2018-2019, consisting of proteins from structures released in 2018 and 2019 with less than 25% identity to any protein published before 2018; (2) a 4-layer convolutional neural network, SecNet, with an input window of +/-14 amino acids which was trained on proteins
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Publication Date: 2020-01-01.
PMCID: PMC7202669
Last updated on Wednesday, August 05, 2020