Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/4454
Title: Protein secondary sturcture prediction using artifical neural networks and support vectors machines
Authors: Jin, Guosheng
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
DRNTU::Engineering::Computer science and engineering::Computing methodologies
Issue Date: 2005
Source: Jin, G. (2005). Protein secondary sturcture prediction using artifical neural networks and support vectors machines. Master’s thesis, Nanyang Technological University, Singapore.
Abstract: 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).
URI: https://hdl.handle.net/10356/4454
DOI: 10.32657/10356/4454
Rights: Nanyang Technological University
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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