In silico discovery of significant pathways in colorectal cancer metastasis using a two-stage optimisation approach

Akutekwe, Arinze, Yang, Shengxiang and Seker, Huseyin (2015) In silico discovery of significant pathways in colorectal cancer metastasis using a two-stage optimisation approach. IET Systems Biology, 9 (6). pp. 294-302. ISSN 1751-8849

Full text not available from this repository.

Abstract

Accurate and reliable modelling of protein-protein interaction networks for complex diseases such as colorectal cancer can help better understand mechanism of diseases and potentially discover new drugs. Different machine learning methods such as empirical mode decomposition combined with least square support vector machine, and discrete Fourier transform have been widely utilised as a classifier and for automatic discovery of biomarkers for the diagnosis of the disease. The existing methods are, however, less efficient as they tend to ignore interaction with the classifier. In this study, the authors propose a two-stage optimisation approach to effectively select biomarkers and discover interactions among them. At the first stage, particle swarm optimisation (PSO) and differential evolution (DE) are used to optimise parameters of support vector machine recursive feature elimination algorithm, and dynamic Bayesian network is then used to predict temporal relationship between biomarkers across two time points. Results show that 18 and 25 biomarkers selected by PSO and DE-based approach, respectively, yields the same accuracy of 97.3% and F1-score of 97.7 and 97.6%, respectively. The stratified analysis reveals that Alpha-2-HS-glycoprotein was a dominant hub gene with multiple interactions to other genes including Fibrinogen alpha chain, which is also a potential biomarker for colorectal cancer.

Item Type:	Article
Subjects:	G900 Others in Mathematical and Computing Sciences
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Becky Skoyles
Date Deposited:	09 Nov 2018 12:41
Last Modified:	11 Oct 2019 18:45
URI:	http://nrl.northumbria.ac.uk/id/eprint/36596

Downloads