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Erschienen in:
Learning Sequential Data with the Help of Linear Systems Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

A Hybrid Recurrent Neural Network/Dynamic Probabilistic Graphical Model Predictor of the Disulfide Bonding State of Cysteines from the Primary Structure of Proteins

verfasst von : Marco Bongini, Vincenzo Laveglia, Edmondo Trentin

Erschienen in: Artificial Neural Networks in Pattern Recognition

Verlag: Springer International Publishing

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Abstract

Cysteines in a protein have a tendency to form mutual disulfide bonds. This affects the secondary and tertiary structure of the protein. Therefore, automatic prediction of the bonding state of cysteines from the primary structure of proteins has long been a relevant task in bioinformatics. The paper investigates the feasibility of a predictor based on a hybrid approach that combines the dynamic encoding capabilities of a recurrent autoencoder with the short-term/long-term dependencies modeling capabilities of a dynamic probabilistic graphical model (a dynamic extension of the hybrid random field). Results obtained using 1797 proteins from the May 2010 version of the Protein Data Bank show an average accuracy of \(85\,\%\) by relying only on the sub-sequences of the residue chains with no additional attributes (like global descriptors, or evolutionary information provided by multiple alignment).

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Vorheriges Kapitel Quad-Tree Based Image Segmentation and Feature Extraction to Recognize Online Handwritten Bangla Characters
Nächstes Kapitel Using Radial Basis Function Neural Networks for Continuous and Discrete Pain Estimation from Bio-physiological Signals
Fußnoten
1
Bearing in mind this sequential dynamics of the model, we will occasionally refer to the RNN as operating “over time”, such that it is fed with t-th residue at “time t”.
 
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Metadaten
Titel
A Hybrid Recurrent Neural Network/Dynamic Probabilistic Graphical Model Predictor of the Disulfide Bonding State of Cysteines from the Primary Structure of Proteins
verfasst von
Marco Bongini
Vincenzo Laveglia
Edmondo Trentin
Copyright-Jahr
2016
Buch
Artificial Neural Networks in Pattern Recognition

Print ISBN: 978-3-319-46181-6

Electronic ISBN: 978-3-319-46182-3

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46182-3_22