An Improved Integrative Random Forest for Gene Regulatory Network Inference of Breast Cancer

Gene Regulatory Network (GRN) inference aims to capture the regulatory influences between the genes and regulatory events in the GRN. Integrative Random Forest for Gene Regulatory Network Inference (iRafNet) is a RF based algorithm which provides a great result in constructing GRN inference by integ...

Full description

Bibliographic Details
Main Authors:	Chandran, Suntharaamurthi, Kohbalan, Moorthy, Mohd Arfian, Ismail, Mohd Zamri, Osman, Mohd Azwan Mohamad, Hamza, Ernawan, Ferda
Format:	Article
Language:	English
Published:	American Scientific Publisher 2018
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/19971/ http://umpir.ump.edu.my/id/eprint/19971/ http://umpir.ump.edu.my/id/eprint/19971/ http://umpir.ump.edu.my/id/eprint/19971/1/43.%20An%20Improved%20Integrative%20Random%20Forest%20for%20Gene%20Regulatory%20Network%20Inference%20for%20Breast%20Cancer1.pdf

id	ump-19971
recordtype	eprints
spelling	ump-199712018-11-21T04:52:35Z http://umpir.ump.edu.my/id/eprint/19971/ An Improved Integrative Random Forest for Gene Regulatory Network Inference of Breast Cancer Chandran, Suntharaamurthi Kohbalan, Moorthy Mohd Arfian, Ismail Mohd Zamri, Osman Mohd Azwan Mohamad, Hamza Ernawan, Ferda QA76 Computer software Gene Regulatory Network (GRN) inference aims to capture the regulatory influences between the genes and regulatory events in the GRN. Integrative Random Forest for Gene Regulatory Network Inference (iRafNet) is a RF based algorithm which provides a great result in constructing GRN inference by integrating multiple data types. Most of the approaches did justify their duty but there are some limitations which don’t allow it to reach its optimal state and needs a very long computational time to construct a GRN inference. Other than that, they do not provide optimal performance. There are redundant genes in the dataset. GRN inference by existing methods has a lower accuracy on benchmark and real dataset. Furthermore, the computational time to produce the GRN inference is very long in the existing methods. To overcome these issues is proposed improved the existing method by adding a gene selection into it. To perform the improvement the existing methods was studied and analyzed on their performance in constructing GRN inference. Improved iRafNet was designed and developed to reduce the computational time to construct the GRN inference gene from the dataset. Finally, the accuracy and computational time of the proposed method was validated and verified with the benchmark and real dataset. Improved iRafNet has proven its performance by having a higher AUC and lower computational time. American Scientific Publisher 2018-11 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/19971/1/43.%20An%20Improved%20Integrative%20Random%20Forest%20for%20Gene%20Regulatory%20Network%20Inference%20for%20Breast%20Cancer1.pdf Chandran, Suntharaamurthi and Kohbalan, Moorthy and Mohd Arfian, Ismail and Mohd Zamri, Osman and Mohd Azwan Mohamad, Hamza and Ernawan, Ferda (2018) An Improved Integrative Random Forest for Gene Regulatory Network Inference of Breast Cancer. Advanced Science Letters, 24 (10). pp. 7566-7571. ISSN 1936-6612 https://doi.org/10.1166/asl.2018.12979 doi: 10.1166/asl.2018.12979
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Chandran, Suntharaamurthi Kohbalan, Moorthy Mohd Arfian, Ismail Mohd Zamri, Osman Mohd Azwan Mohamad, Hamza Ernawan, Ferda An Improved Integrative Random Forest for Gene Regulatory Network Inference of Breast Cancer
description	Gene Regulatory Network (GRN) inference aims to capture the regulatory influences between the genes and regulatory events in the GRN. Integrative Random Forest for Gene Regulatory Network Inference (iRafNet) is a RF based algorithm which provides a great result in constructing GRN inference by integrating multiple data types. Most of the approaches did justify their duty but there are some limitations which don’t allow it to reach its optimal state and needs a very long computational time to construct a GRN inference. Other than that, they do not provide optimal performance. There are redundant genes in the dataset. GRN inference by existing methods has a lower accuracy on benchmark and real dataset. Furthermore, the computational time to produce the GRN inference is very long in the existing methods. To overcome these issues is proposed improved the existing method by adding a gene selection into it. To perform the improvement the existing methods was studied and analyzed on their performance in constructing GRN inference. Improved iRafNet was designed and developed to reduce the computational time to construct the GRN inference gene from the dataset. Finally, the accuracy and computational time of the proposed method was validated and verified with the benchmark and real dataset. Improved iRafNet has proven its performance by having a higher AUC and lower computational time.
format	Article
author	Chandran, Suntharaamurthi Kohbalan, Moorthy Mohd Arfian, Ismail Mohd Zamri, Osman Mohd Azwan Mohamad, Hamza Ernawan, Ferda
author_facet	Chandran, Suntharaamurthi Kohbalan, Moorthy Mohd Arfian, Ismail Mohd Zamri, Osman Mohd Azwan Mohamad, Hamza Ernawan, Ferda
author_sort	Chandran, Suntharaamurthi
title	An Improved Integrative Random Forest for Gene Regulatory Network Inference of Breast Cancer
title_short	An Improved Integrative Random Forest for Gene Regulatory Network Inference of Breast Cancer
title_full	An Improved Integrative Random Forest for Gene Regulatory Network Inference of Breast Cancer
title_fullStr	An Improved Integrative Random Forest for Gene Regulatory Network Inference of Breast Cancer
title_full_unstemmed	An Improved Integrative Random Forest for Gene Regulatory Network Inference of Breast Cancer
title_sort	improved integrative random forest for gene regulatory network inference of breast cancer
publisher	American Scientific Publisher
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/19971/ http://umpir.ump.edu.my/id/eprint/19971/ http://umpir.ump.edu.my/id/eprint/19971/ http://umpir.ump.edu.my/id/eprint/19971/1/43.%20An%20Improved%20Integrative%20Random%20Forest%20for%20Gene%20Regulatory%20Network%20Inference%20for%20Breast%20Cancer1.pdf
first_indexed	2023-09-18T22:28:38Z
last_indexed	2023-09-18T22:28:38Z
_version_	1777416135604436992

An Improved Integrative Random Forest for Gene Regulatory Network Inference of Breast Cancer

Similar Items