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Updated: Jul 10, 2025

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A General Framework for Identifying Hierarchical Interactions and Its Application to Genomics Data.

Zhang Xiao1, Shi Xingjie1, Liu Yiming2

  • 1KLATASDS-MOE, Academy of Statistics and Interdisciplinary Sciences, East China Normal University, China.

Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|November 27, 2023
PubMed
Summary
This summary is machine-generated.

We introduce HierFabs, a fast computational method for analyzing hierarchical interactions. It efficiently handles complex data relationships, outperforming existing methods in simulations and real-world data analysis.

Keywords:
Forward and backward stagewiseHigh-dimensional modelingInteraction analysisLassoPenalized selection

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Area of Science:

  • Statistics
  • Computational Biology
  • Bioinformatics

Background:

  • Analyzing hierarchical interactions is complex due to numerous effects and the need to maintain hierarchy.
  • Existing two-stage methods are simple but ignore joint effects, while joint methods are accurate but computationally expensive.

Purpose of the Study:

  • To develop a computationally efficient method for analyzing hierarchical interactions.
  • To address the limitations of existing two-stage and joint analysis methods.

Main Methods:

  • Developed HierFabs, a novel computational method based on stagewise algorithms.
  • Incorporated new forward and backward steps to accommodate hierarchy without extra constraints.
  • Investigated the theoretical optimality of HierFabs sequences.

Main Results:

  • HierFabs achieves computational efficiency comparable to two-stage methods.
  • The method naturally handles strong and weak hierarchy, simplifying and accelerating optimization.
  • Simulations demonstrated superior performance compared to existing methods.
  • HierFabs showed competitive practical performance on TCGA melanoma data.

Conclusions:

  • HierFabs offers a computationally efficient and broadly applicable solution for hierarchical interaction analysis.
  • The method provides a practical alternative for analyzing complex biological data, such as genomic datasets.