Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners
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چکیده
منابع مشابه
Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners
MOTIVATION Accurate prediction of protein contact maps is an important step in computational structural proteomics. Because contact maps provide a translation and rotation invariant topological representation of a protein, they can be used as a fundamental intermediary step in protein structure prediction. RESULTS We develop a new set of flexible machine learning architectures for the predict...
متن کاملPrediction of Contact Maps by Recurrent Neural Network Architectures and Hidden Context Propagation From All Four Cardinal Corners
ABSTRACT Motivation: Accurate prediction of protein contact maps is an important step in computational structural proteomics. Because contact maps provide a translation and rotation invariant topological representation of a protein, they can be used as a fundamental intermediary step in protein structure prediction. Results: We develop a new set of flexible machine learning architectures for th...
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BACKGROUNDS Despite continuing progress in X-ray crystallography and high-field NMR spectroscopy for determination of three-dimensional protein structures, the number of unsolved and newly discovered sequences grows much faster than that of determined structures. Protein modeling methods can possibly bridge this huge sequence-structure gap with the development of computational science. A grand ...
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Contact maps of proteins are predicted with neural network-based methods, using as input codings of increasing complexity including evolutionary information, sequence conservation, correlated mutations and predicted secondary structures. Neural networks are trained on a data set comprising the contact maps of 173 non-homologous proteins as computed from their well resolved three-dimensional str...
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ژورنال
عنوان ژورنال: Bioinformatics
سال: 2002
ISSN: 1367-4803,1460-2059
DOI: 10.1093/bioinformatics/18.suppl_1.s62