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Related Experiment Video

Updated: Jun 16, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Advancing biomedical data analytics using explainable neural network-based learning model for progressive

S Praveena1, E Laxmi Lydia2, Suresh Betam3

  • 1Electronic and Communication Engineering, Mahatma Gandhi Institute of Technology, Gandipet, Hyderabad, Telangana, India.

Scientific Reports
|June 13, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an Explainable Neural Network-Driven Learning Model for Neurodegenerative Disorder Diagnosis (XNNLM-NDD) to improve Huntington's disease detection. The model achieves 96.50% accuracy by interpreting clinical data, enhancing trust in AI for neurodegenerative disorder diagnosis.

Keywords:
Cycle-Norm-Adam optimizerDeep learningExplainable artificial intelligenceHuntington’s diseaseHybrid feature selection

Related Experiment Videos

Last Updated: Jun 16, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Huntington's disease (HD) is an inherited neurodegenerative disorder caused by huntingtin (HTT) gene mutations.
  • Current artificial intelligence (AI) models for HD diagnosis, while effective, often lack transparency, hindering clinical trust.
  • Explainable AI (XAI) is crucial for understanding AI decision-making in healthcare applications.

Purpose of the Study:

  • To develop an Explainable Neural Network-Driven Learning Model for Neurodegenerative Disorder Diagnosis (XNNLM-NDD).
  • To enhance the accuracy and interpretability of AI-driven HD diagnosis using clinical data.
  • To address the 'black box' problem in deep learning models for medical applications.

Main Methods:

  • Feature selection using a hybrid Minimum Redundancy Maximum Relevance (mRMR) and ReliefF approach.
  • Classification using a feature tokenizer-transformer model for complex interactions.
  • Optimization via the Cycle-Norm-Adam algorithm and interpretability through SHAP (SHapley Additive exPlanations).

Main Results:

  • The XNNLM-NDD model achieved a diagnostic accuracy of 96.50% on the Huntington Disease Dataset.
  • The model effectively identified key clinical features contributing to HD diagnosis.
  • Demonstrated superior performance compared to existing techniques in progressive neurodegenerative disorder diagnosis.

Conclusions:

  • The proposed XNNLM-NDD model offers a transparent and accurate approach for diagnosing neurodegenerative disorders like HD.
  • Explainable AI methods significantly improve the reliability and clinical adoption of AI in healthcare.
  • This work paves the way for more trustworthy AI-powered diagnostic tools in neurology.