Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles

By Gianluca Pollastri, Dariusz Przybylski, Burkhard Rost, Pierre Baldi. 2002

809 citations recorded by the source corpus.

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In Molecular Biology

Citations in the corpus

Learning to Diagnose with LSTM Recurrent Neural Networks (2015, methodology): “Recurrent Neural Networks (RNNs), in particular those based on Long Short-Term Memory (LSTM) (Hochreiter & Schmidhuber, 1997), model varying-length sequential data, achieving state-of-the-art results for problems spanning natural language processing, image captioning, handwriting recognition, and ge”
A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction (2014, methodology): “Neural networks have been effectively used in a variety of prediction algorithms, including speech and face recognition as well as the aforementioned usage in protein secondary structure prediction[11-22, 24, 33-36].”
Template-based protein structure modeling using the RaptorX web server (2012, background): “31), which to the best of our knowledge is the only publicly available server providing eight-state secondary structure prediction.”
RaptorX-Property: a web server for protein structure property prediction (2016, background): “The background distribution of the eight states H, E, L, T, S, G, B, I is around 34:21:20:11:9:4:1:0 (20).”
PRALINE: a multiple sequence alignment toolbox that integrates homology-extended and secondary structure information (2005, background): “01 (28), YASPIN (20), PSIPRED (19), JNET (29) and PREDATOR (30,31)] to predict the secondary structure of the input sequences.”
Protein 8-class secondary structure prediction using conditional neural fields (2010, background): “To evaluate the Q8 accuracy of SSPro8, we submit the proteins sequences to the SSpro8 server and parse results returned by the server.”
Deep Spatio-Temporal Architectures and Learning for Protein Structure Prediction (2012, background): “The secondary structure is predicted with SSPRO [18] and the solvent accessibility with ACCPRO [19].”
Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networks (2015, methodology): “RNNs have also been used for prediction problems in genomics [8, 10, 9].”
Deep learning methods in protein structure prediction (2020, background): “Subsequent versions of SSpro increased the size of the training datasets and networks [71].”
Neural Networks and Deep Learning (2018, neutral): “Bidirectional recurrent networks and LSTMs for protein structure prediction, handwriting recognition, translation, and speech recognition are discussed in [20, 154, 155, 157, 378, 477].”