Department of Statistics, Mathematics and Insurance, University of Ain Shams, Cairo 11566, Egypt.
School of Computing, Edinburgh Napier University, Edinburgh EH11 4BN, UK.
Sensors (Basel). 2021 Mar 21;21(6):2190. doi: 10.3390/s21062190.
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.
基于机器学习（ML）的算法在癌症诊断中发挥着重要作用，并越来越多地被用于辅助临床决策。然而，这些算法通常作为“黑箱”运作，其决策过程不明确。最近，已经应用了一些技术来帮助我们了解特定的 ML 模型是如何工作的，并解释其输出的合理性。本研究旨在确定某种癌症为何具有特定的表型特征。癌症导致细胞失调，需要全面考虑癌症调节剂。这将增加我们对疾病本质的理解，并有助于发现针对各种癌症类型和阶段的更有效的诊断、预后和治疗方法。我们的研究提出了一种新的可解释性分析方法，用于表示非小细胞肺癌中的肿瘤发生的潜在生物标志物。这些生物标志物中的许多标志物已知在各种治疗途径后出现。通过对 mRNA 调节剂、ncRNA 调节剂和 mRNA-ncRNA 耦合调节剂的新数学公式，实现了增强分析。使用由耦合反应偏微分方程组成的系统，在二维空间域中逼近过渡状态的时间基因表达谱，然后收敛到稳定状态。模拟实验表明，所提出的数学基因表达谱代表了这些致癌基因群体丰度的最佳拟合。未来，我们提出的解决方案可以通过将 ML 模型应用于发现基因调控系统中的未知动态，从而开发出替代的可解释方法。
