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

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Singh, B, Doborjeh, M, Doborjeh, Z, Budhraja, S, Tan, S, Sumich, A ORCID: https://orcid.org/0000-0003-4333-8442, Goh, W, Lee, J, Lai, E and Kasabov, N, 2023. Constrained neuro fuzzy inference methodology for explainable personalised modelling with applications on gene expression data. Scientific Reports, 13 (1): 456. ISSN 2045-2322

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Official URL: https://doi.org/10.1038/s41598-022-27132-8

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 from high dimensional datasets which are structurally and semantically interpretable. Case studies on two mental health related datasets (schizophrenia and bipolar disorders) have shown that the relatively short and simple personalised fuzzy rules provided enhanced interpretability as well as better classification performance compared to other commonly used machine learning methods. Performance test on a cancer dataset also showed that PCNFI matches previous benchmarks. Insights from our approach also indicated the importance of two genes (ATRX and TSPAN2) as possible biomarkers for early differentiation of ultra-high risk, bipolar and healthy individuals. These genes are linked to cognitive ability and impulsive behaviour. Our findings suggest a significant starting point for further research into the biological role of cognitive and impulsivity-related differences. With potential applications across bio-medical research, the proposed PCNFI method is promising for diagnosis, prognosis, and the design of personalised treatment plans for better outcomes in the future.

Item Type:	Journal article
Publication Title:	Scientific Reports
Creators:	Singh, B., Doborjeh, M., Doborjeh, Z., Budhraja, S., Tan, S., Sumich, A., Goh, W., Lee, J., Lai, E. and Kasabov, N.
Publisher:	Springer Science and Business Media LLC
Date:	9 January 2023
Volume:	13
Number:	1
ISSN:	2045-2322
Identifiers:	Number Type 10.1038/s41598-022-27132-8 DOI 1635241 Other
Rights:	© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Divisions:	Schools > School of Social Sciences
Record created by:	Laura Ward
Date Added:	19 Jan 2023 13:44
Last Modified:	19 Jan 2023 13:44
URI:	https://irep.ntu.ac.uk/id/eprint/47968