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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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High-dimensional biomarker identification for interpretable disease prediction via machine learning models.

Yifan Dai1, Di Wu1,2, Ian Carroll3

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.

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Summary

We developed a new framework, HiFIT, to identify important omics biomarkers for complex diseases. This method improves disease understanding and precision medicine by analyzing high-dimensional data effectively.

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Genomics

Background:

  • Integrating omics and clinical data is crucial for understanding complex human diseases.
  • High dimensionality and complex associations pose analytical challenges for biomarker discovery.
  • Accurate identification of omics biomarkers is vital for early diagnosis and precision medicine.

Purpose of the Study:

  • To propose a novel framework, HiFIT, for high-dimensional feature importance testing.
  • To address the challenges in integrating omics and clinical data for disease research.
  • To enhance the identification of key omics biomarkers and improve outcome prediction.

Main Methods:

  • Developed Hybrid Feature Screening (HFS) for data-driven biomarker identification.
  • Employed a permutation-based feature importance test with machine learning for flexible modeling.
  • Utilized an ensemble approach to refine candidate features for downstream analysis.

Main Results:

  • HiFIT demonstrates superior performance in outcome prediction and feature importance identification.
  • Validated through simulations and applications to microbiome and gene-expression data.
  • Successfully identified key molecular biomarkers associated with disease outcomes.

Conclusions:

  • HiFIT provides a robust framework for analyzing high-dimensional omics data in complex diseases.
  • The method facilitates a deeper understanding of disease mechanisms and aids in precision medicine.
  • An R package for HiFIT is publicly available for broader research application.