Investigation of image processing algorithms for medical application

Gene Regulatory Network is the network that constitute the interaction between genes. There is a need to find an effective way for the discovery of gene regulatory network which will provide better information for further advancement in biotechnology and bioinformatics. One of the prominent approach...

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Main Author: Tijani, Zhafir Aglna
Other Authors: Mohammed Yakoob Siyal
Format: Final Year Project
Language:English
Published: 2015
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Online Access:http://hdl.handle.net/10356/63880
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-638802023-07-07T16:30:50Z Investigation of image processing algorithms for medical application Tijani, Zhafir Aglna Mohammed Yakoob Siyal School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Gene Regulatory Network is the network that constitute the interaction between genes. There is a need to find an effective way for the discovery of gene regulatory network which will provide better information for further advancement in biotechnology and bioinformatics. One of the prominent approach to analyse GRN is Granger Causality Analysis. This project tries to perform comparative study between different implementation of granger causality. Specifically Multivariate Granger Causality (MVGC), Lasso Granger Causality, and Copula Granger Causality. These three methods are previously researched by other researcher, but never been compared side to side under the same condition. The project was implemented with experimental framework that utilizes 3 control variables and 7 metrics. These 7 metrics are the basis for the comparative study of the project itself. Based on the findings in the experiment, overall score favours MVGC methods as the best algorithm. However in some condition, Lasso and Copula score exceeds MVGC which indicates that the results are conditional, not absolute. It could be said that each method has the condition that maximizes their performance. This project provides analysis of these situations based on the experiment result. Bachelor of Engineering 2015-05-19T09:17:14Z 2015-05-19T09:17:14Z 2015 2015 Final Year Project (FYP) http://hdl.handle.net/10356/63880 en Nanyang Technological University 66 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Tijani, Zhafir Aglna
Investigation of image processing algorithms for medical application
description Gene Regulatory Network is the network that constitute the interaction between genes. There is a need to find an effective way for the discovery of gene regulatory network which will provide better information for further advancement in biotechnology and bioinformatics. One of the prominent approach to analyse GRN is Granger Causality Analysis. This project tries to perform comparative study between different implementation of granger causality. Specifically Multivariate Granger Causality (MVGC), Lasso Granger Causality, and Copula Granger Causality. These three methods are previously researched by other researcher, but never been compared side to side under the same condition. The project was implemented with experimental framework that utilizes 3 control variables and 7 metrics. These 7 metrics are the basis for the comparative study of the project itself. Based on the findings in the experiment, overall score favours MVGC methods as the best algorithm. However in some condition, Lasso and Copula score exceeds MVGC which indicates that the results are conditional, not absolute. It could be said that each method has the condition that maximizes their performance. This project provides analysis of these situations based on the experiment result.
author2 Mohammed Yakoob Siyal
author_facet Mohammed Yakoob Siyal
Tijani, Zhafir Aglna
format Final Year Project
author Tijani, Zhafir Aglna
author_sort Tijani, Zhafir Aglna
title Investigation of image processing algorithms for medical application
title_short Investigation of image processing algorithms for medical application
title_full Investigation of image processing algorithms for medical application
title_fullStr Investigation of image processing algorithms for medical application
title_full_unstemmed Investigation of image processing algorithms for medical application
title_sort investigation of image processing algorithms for medical application
publishDate 2015
url http://hdl.handle.net/10356/63880
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