Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/35725
Title: Protein structure prediction using bidirectional neural networks
Authors: Chen, Jin Miao
Keywords: DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences
Issue Date: 2007
Source: Chen, J. M. (2007). Protein structure prediction using bidirectional neural networks. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: This thesis is focused on protein secondary structure (PSS) prediction which is one of the most important problems in bioinformatics. Most of existing prediction methods either use a sliding fixed-sized window centered on the residue of interest or train on single positions of the sequence to classify mutually independent secondary structures. They are unable to take into account long-range interactions between amino acids and strong correlations between secondary structure (SS) elements. In this thesis, we propose and develop three novel prediction models, namely bidirectional long short-term memory (BLSTM), bidirectional segmented-memory recurrent neural network (BSMRNN) and cascaded bidirectional recurrent neural network (Cascaded BRNN), which are all variants of bidirectional recurrent neural network (BRNN).
Description: 211 p.
URI: https://hdl.handle.net/10356/35725
DOI: 10.32657/10356/35725
Schools: School of Computer Engineering 
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Theses

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