Novel algorithms for inferring granger causality using biological data

Biological network diagrams provide a natural means to characterize the association between biological entities such as genes, proteins, or brain regions. The understanding of these biological networks provides a range of information from systematic behaviours to disease susceptibility and its treat...

Full description

Saved in:
Bibliographic Details
Main Author: Furqan, Mohammad Shaheryar
Other Authors: Mohammed Yakoob Siyal
Format: Theses and Dissertations
Language:English
Published: 2017
Subjects:
Online Access:http://hdl.handle.net/10356/72683
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-72683
record_format dspace
spelling sg-ntu-dr.10356-726832023-07-04T17:29:48Z Novel algorithms for inferring granger causality using biological data Furqan, Mohammad Shaheryar Mohammed Yakoob Siyal School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Biological network diagrams provide a natural means to characterize the association between biological entities such as genes, proteins, or brain regions. The understanding of these biological networks provides a range of information from systematic behaviours to disease susceptibility and its treatment. Currently, researchers from diverse research backgrounds are trying to model biological networks using various analytical modalities to improve understanding and prediction of biological networks. However, with the advancement of technology, the inference of biological networks from high-throughput data has received immense consideration throughout the last decade, and is a major area of research in systems biology. One of the conventional methods used for inferring biological networks from time series data is Granger causality, which analyses the causal link between the variables and can be used to map the associations in the form of a network diagram using graph theory. However, when using high-throughput data, current implementations of Granger causality face limitations, especially while handling high dimensionality and non-linear relationships among correlated variables. In order to minimize these impediments, we have proposed three heuristic techniques, and two variants of bi-directional Granger causality. We call the heuristic techniques random Forest Granger causality (RFGC), Elastic-net copula Granger causality (ECGC) and random forest copula Granger causality. The bi-directional Granger causality methods include forward backward pairwise Granger causality (FCPGC) and bi-directional random forest Granger causality (BRFGC). All these techniques are capable of handling both high- and low-dimensional data. The effectiveness of these methods is demonstrated through extensive experimentation using simulated and real datasets. We also used these methods on two real world biological applications: in mapping a gene network, commonly involved in cancer using a HeLa cancer cell dataset; and in an effective brain network, normally involved in human deductive reasoning.   Doctor of Philosophy (EEE) 2017-09-25T06:33:58Z 2017-09-25T06:33:58Z 2017 Thesis Furqan, M. S. (2017). Novel algorithms for inferring granger causality using biological data. Doctoral thesis, Nanyang Technological University, Singapore. http://hdl.handle.net/10356/72683 10.32657/10356/72683 en 113 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::Electronic systems::Signal processing
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Furqan, Mohammad Shaheryar
Novel algorithms for inferring granger causality using biological data
description Biological network diagrams provide a natural means to characterize the association between biological entities such as genes, proteins, or brain regions. The understanding of these biological networks provides a range of information from systematic behaviours to disease susceptibility and its treatment. Currently, researchers from diverse research backgrounds are trying to model biological networks using various analytical modalities to improve understanding and prediction of biological networks. However, with the advancement of technology, the inference of biological networks from high-throughput data has received immense consideration throughout the last decade, and is a major area of research in systems biology. One of the conventional methods used for inferring biological networks from time series data is Granger causality, which analyses the causal link between the variables and can be used to map the associations in the form of a network diagram using graph theory. However, when using high-throughput data, current implementations of Granger causality face limitations, especially while handling high dimensionality and non-linear relationships among correlated variables. In order to minimize these impediments, we have proposed three heuristic techniques, and two variants of bi-directional Granger causality. We call the heuristic techniques random Forest Granger causality (RFGC), Elastic-net copula Granger causality (ECGC) and random forest copula Granger causality. The bi-directional Granger causality methods include forward backward pairwise Granger causality (FCPGC) and bi-directional random forest Granger causality (BRFGC). All these techniques are capable of handling both high- and low-dimensional data. The effectiveness of these methods is demonstrated through extensive experimentation using simulated and real datasets. We also used these methods on two real world biological applications: in mapping a gene network, commonly involved in cancer using a HeLa cancer cell dataset; and in an effective brain network, normally involved in human deductive reasoning.  
author2 Mohammed Yakoob Siyal
author_facet Mohammed Yakoob Siyal
Furqan, Mohammad Shaheryar
format Theses and Dissertations
author Furqan, Mohammad Shaheryar
author_sort Furqan, Mohammad Shaheryar
title Novel algorithms for inferring granger causality using biological data
title_short Novel algorithms for inferring granger causality using biological data
title_full Novel algorithms for inferring granger causality using biological data
title_fullStr Novel algorithms for inferring granger causality using biological data
title_full_unstemmed Novel algorithms for inferring granger causality using biological data
title_sort novel algorithms for inferring granger causality using biological data
publishDate 2017
url http://hdl.handle.net/10356/72683
_version_ 1772829076094976000