A novel coupled reaction-diffusion system for explainable gene expression profiling

Farouq, M. W. et al. (2021) A novel coupled reaction-diffusion system for explainable gene expression profiling. Sensors, 21(6), 2190. (doi: 10.3390/s21062190) (PMID:33801002) (PMCID:PMC8003942)

Abstract

Machine learning (ML)-based algorithms are playing an important role in cancer diagnosis and are increasingly being used to aid clinical decision-making. However, these commonly operate as ‘black boxes’ and it is unclear how decisions are derived. Recently, techniques have been applied to help us understand how specific ML models work and explain the rational for outputs. This study aims to determine why a given type of cancer has a certain phenotypic characteristic. Cancer results in cellular dysregulation and a thorough consideration of cancer regulators is required. This would increase our understanding of the nature of the disease and help discover more effective diagnostic, prognostic, and treatment methods for a variety of cancer types and stages. Our study proposes a novel explainable analysis of potential biomarkers denoting tumorigenesis in non-small cell lung cancer. A number of these biomarkers are known to appear following various treatment pathways. An enhanced analysis is enabled through a novel mathematical formulation for the regulators of mRNA, the regulators of ncRNA, and the coupled mRNA–ncRNA regulators. Temporal gene expression profiles are approximated in a two-dimensional spatial domain for the transition states before converging to the stationary state, using a system comprised of coupled-reaction partial differential equations. Simulation experiments demonstrate that the proposed mathematical gene-expression profile represents a best fit for the population abundance of these oncogenes. In future, our proposed solution can lead to the development of alternative interpretable approaches, through the application of ML models to discover unknown dynamics in gene regulatory systems.

• Item Type: Articles
• Additional Information: Funding: A. Sheikh is supported by BREATHE—The Health Data Research (HDR) Hub for Respiratory Health [MC_PC_19004], which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through HDR UK. A. Hussain is supported by the UK Government’s Engineering and Physical Sciences Research Council (EPSRC) grants (Ref. EP/T021063/1, EP/T024917/1).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Shaikh, Dr Mohammed Guftar

Creator Roles

Shaikh, M. G.: Writing – review and editing

• Authors: Farouq, M. W., Boulila, W., Hussain, Z., Rashid, A., Shah, M., Hussain, S., Ng, N., Ng, D., Hanif, H., Shaikh, M. G., Sheikh, A., and Hussain, A.
• College/School: College of Medical Veterinary and Life Sciences > School of Medicine, Dentistry & Nursing
• Journal Name: Sensors
• Publisher: MDPI
• ISSN: 1424-8220
• ISSN (Online): 1424-8220
• Copyright Holders: Copyright © 2021 by the author(s)
• First Published: First published in Sensors 21(6):2190
• Publisher Policy: Reproduced under a creative commons licence