Protein secondary sturcture prediction using artifical neural networks and support vectors machines

Protein secondary sturcture prediction using artifical neural networks and support vectors machines

In this thesis, a novel Hybrid Neural Networks Predictor (HNNP) system for the Protein Secondary Structure Prediction (PSSP) problem is described. By explor- ing such a new hybrid system, the intention is to investigate the feasibility of the HNNP in PSSP and even achieve improvements over existing...

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Main Author: Jin, Guosheng
Other Authors: Wang Lipo
Format: Theses and Dissertations
Published: 2008
Subjects:
DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
DRNTU::Engineering::Computer science and engineering::Computing methodologies
Online Access:https://hdl.handle.net/10356/4454
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-44542023-07-04T16:59:32Z Protein secondary sturcture prediction using artifical neural networks and support vectors machines Jin, Guosheng Wang Lipo School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems DRNTU::Engineering::Computer science and engineering::Computing methodologies In this thesis, a novel Hybrid Neural Networks Predictor (HNNP) system for the Protein Secondary Structure Prediction (PSSP) problem is described. By explor- ing such a new hybrid system, the intention is to investigate the feasibility of the HNNP in PSSP and even achieve improvements over existing methods. The proposed system is a cascaded network of the Radial Basis Function Neural Net- work (RBFNN) and the Multi-Layer Perceptron Neural Network (MLPNN). MASTER OF ENGINEERING (EEE) 2008-09-17T09:51:50Z 2008-09-17T09:51:50Z 2005 2005 Thesis Jin, G. (2005). Protein secondary sturcture prediction using artifical neural networks and support vectors machines. Master’s thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/4454 10.32657/10356/4454 Nanyang Technological University application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
topic DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
DRNTU::Engineering::Computer science and engineering::Computing methodologies
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
DRNTU::Engineering::Computer science and engineering::Computing methodologies
Jin, Guosheng
Protein secondary sturcture prediction using artifical neural networks and support vectors machines
description In this thesis, a novel Hybrid Neural Networks Predictor (HNNP) system for the Protein Secondary Structure Prediction (PSSP) problem is described. By explor- ing such a new hybrid system, the intention is to investigate the feasibility of the HNNP in PSSP and even achieve improvements over existing methods. The proposed system is a cascaded network of the Radial Basis Function Neural Net- work (RBFNN) and the Multi-Layer Perceptron Neural Network (MLPNN).
author2 Wang Lipo
author_facet Wang Lipo
Jin, Guosheng
format Theses and Dissertations
author Jin, Guosheng
author_sort Jin, Guosheng
title Protein secondary sturcture prediction using artifical neural networks and support vectors machines
title_short Protein secondary sturcture prediction using artifical neural networks and support vectors machines
title_full Protein secondary sturcture prediction using artifical neural networks and support vectors machines
title_fullStr Protein secondary sturcture prediction using artifical neural networks and support vectors machines
title_full_unstemmed Protein secondary sturcture prediction using artifical neural networks and support vectors machines
title_sort protein secondary sturcture prediction using artifical neural networks and support vectors machines
publishDate 2008
url https://hdl.handle.net/10356/4454
_version_ 1772826787791765504

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