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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Federated unsupervised random forest for privacy-preserving patient stratification.

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This study introduces unsupervised random forests for multi-omics clustering to improve patient stratification in precision medicine. Federated computing enhances clustering performance while preserving data privacy.

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Area of Science:

  • Computational biology and bioinformatics
  • Machine learning in healthcare
  • Precision medicine methodologies

Background:

  • Effective patient stratification and disease subtyping are crucial for precision medicine, requiring advanced methods for multi-omics data analysis.
  • Clinical datasets are often small and fragmented across institutions, posing challenges for big data approaches due to privacy concerns.
  • Machine learning techniques are vital for medical advancements but are hindered by data sharing limitations.

Purpose of the Study:

  • To develop an innovative framework for advancing precision medicine using unsupervised clustering on multi-omics data.
  • To address privacy concerns in medical data sharing through the integration of federated computing.
  • To enhance patient subgroup identification and disease subtyping capabilities.

Main Methods:

  • A novel multi-omics clustering approach employing unsupervised random forests.
  • Federated execution of the random forest methodology to ensure data privacy.
  • Validation on benchmark machine learning datasets and The Cancer Genome Atlas (TCGA) cancer data.

Main Results:

  • The unsupervised random forest enables identification of cluster-specific feature importance, revealing key molecular drivers of patient groups.
  • The federated approach is competitive with state-of-the-art methods for disease subtyping.
  • The methodology significantly enhances the interpretability of identified patient clusters.
  • Federated computing demonstrates potential for improving local clustering performance.

Conclusions:

  • The proposed framework offers a powerful tool for precision medicine by enabling robust patient stratification and disease subtyping.
  • The integration of unsupervised random forests and federated computing provides a privacy-preserving solution for multi-omics data analysis.
  • The R-package facilitates the application of these advanced clustering techniques in research and clinical settings.