Gene regulatory network construction of ovarian cancer based on passing attributes between network for data assimilation

Yeo, Zhu Ern Isaac and Moorthy, Kohbalan and Machap, Logenthiran and Mohd Saberi, Mohamad and Jamaludin, Sallim (2020) Gene regulatory network construction of ovarian cancer based on passing attributes between network for data assimilation. In: 2020 8th International Conference on Information Technology and Multimedia, ICIMU 2020, 24 - 25 August 2020 , Selangor. 251 -255. (9243432). ISBN 9781728173108

[img]
Preview
Pdf
Gene regulatory network construction of ovarian cancer based .pdf

Download (145kB) | Preview
[img] Pdf
Gene regulatory network construction of ovarian cancer based_FULL.pdf
Restricted to Repository staff only

Download (606kB) | Request a copy

Abstract

In the field of cancer informatics, there are computational methods or approach exists to share the same goal, which is to unravel the interactions between genes through the effort of gene regulatory network (GRN) inference and construction. Even now, such a complex task has always been challenging and at the same time, this challenge becomes a motivation for new methods to be invented. Hence, the development of PYPANDA, which is a new method for applying the assimilation of several different datasets input for the construction of the gene regulatory network. Moreover, this integration model is capable of redeeming information that was lost when using other methods that only utilize a single dataset, thus having an innate capacity of predicting a more accurate interaction between genes. The proposed improvement of PYPANDA in this article has been able to filter and determine the most informative or significant genes for the construction of the GRN. With this, the differences between the prior network and the improved PYPANDA network can be specified. As such, two new relationships between the highly informative genes that have not been identified before were successfully identified.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Ovarian cancer; Gene regulatory network; Python; Informative genes; Machine learning
Subjects: Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Institute of Postgraduate Studies
Faculty of Computing
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 07 Feb 2022 07:59
Last Modified: 07 Feb 2022 07:59
URI: http://umpir.ump.edu.my/id/eprint/32040
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item