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Structured feature ranking for genomic marker identification accommodating multiple types of networks.

Yeheng Ge1, Tao Li1, Xingdong Feng1

  • 1School of Statistics and Data Science, Shanghai University of Finance and Economics, 777 Guoding Road, Shanghai 200433, China.

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This study introduces a novel network-structured feature ranking method to identify genomic markers for complex diseases. It effectively incorporates predictor dependencies, improving upon marginal methods for better disease association discovery.

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graph Laplacian regularizationhigh dimensional data analysisnetwork structured analysis

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Area of Science:

  • Genomics
  • Statistical Bioinformatics
  • Computational Biology

Background:

  • Complex diseases involve intricate biological processes and interactions among genomic predictors.
  • Existing feature ranking methods often overlook predictor dependencies, limiting their effectiveness in identifying disease-associated genomic markers.
  • Understanding network structures among molecular predictors is crucial for accurate disease association studies.

Purpose of the Study:

  • To propose a novel structured feature ranking method that effectively incorporates network structures among genomic predictors.
  • To identify robust genomic markers associated with complex disease development, progression, and treatment response.
  • To improve upon existing marginal feature ranking methods by accounting for predictor dependencies.

Main Methods:

  • Developed a structured feature ranking method utilizing Laplacian regularization to accommodate predictor network structures.
  • Investigated multiple network scenarios, including a priori known and data-dependently estimated networks.
  • Rigorously analyzed the impact of noise and uncertainty in network structures, with methods for parameter selection to control these effects.

Main Results:

  • The proposed network-structured measure demonstrates sure screening properties with a faster convergence rate compared to marginal measures.
  • Theoretical results rigorously establish the method's validity and performance.
  • Simulations and analysis of The Cancer Genome Atlas melanoma data confirm improved finite sample performance and practical utility.

Conclusions:

  • The proposed Laplacian regularized feature ranking method effectively integrates genomic network structures for identifying disease-associated markers.
  • This approach offers broad applicability and superior performance over traditional marginal methods, especially in the presence of complex predictor interactions.
  • The method provides a powerful tool for genomic marker discovery in complex diseases, enhancing understanding of disease mechanisms and treatment strategies.