A Novel Coupled Reaction-Diffusion System for Explainable Gene Expression Profiling.

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 explainab

SUBMITTER: Farouq MW 

PROVIDER: S-EPMC8003942 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature