Constrained neuro fuzzy inference methodology for explainable personalised modelling with applications on gene expression data.

ABSTRACT: Interpretable machine learning models for gene expression datasets are important for understanding the decision-making process of a classifier and gaining insights on the underlying molecular processes of genetic conditions. Interpretable models can potentially support early diagnosis before full disease manifestation. This is particularly important yet, challenging for mental health. We hypothesise this is due to extreme heterogeneity issues which may be overcome and explained by personalised modelling techniques. Thus far, most machine learning methods applied to gene expression datasets, including deep neural networks, lack personalised interpretability. This paper proposes a new methodology named personalised constrained neuro fuzzy inference (PCNFI) for learning personalised rules fro

SUBMITTER: Singh B 

PROVIDER: S-EPMC9829920 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature