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

Balkaran Singh, Maryam Doborjeh, Zohreh Doborjeh, Sugam Budhraja, Samuel Tan, Alexander Sumich, Wilson Goh, Jimmy Lee, Edmund Lai, Nikola Kasabov

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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.

Original languageEnglish
Article number456
Pages (from-to)1-15
Number of pages15
JournalScientific Reports
Early online date9 Jan 2023
Publication statusPublished online - 9 Jan 2023

Bibliographical note

Funding Information:
This research is supported by the MBIE Catalyst: Strategic—New Zealand-Singapore Data Science Research Programme Funding and the National Research Foundation, Singapore under its Industry Alignment Fund—Pre-positioning (IAF-PP) Funding Initiative. The LYRIKS study was supported by the National Research Foundation Singapore under the National Medical Research Council Translational and Clinical Research Flagship Programme (NMRC/TCR/003/2008). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.

Publisher Copyright:
© 2023, The Author(s).


  • fuzzy rules
  • personalised modelling
  • gene expression data
  • bi-polar disease
  • psychosis
  • explianable AI
  • neuro-fuzzy inference
  • neuro-fuzzy systems


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