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Updated: May 9, 2026

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method
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Published on: May 21, 2018

Efficient methods for overlapping group lasso.

Lei Yuan1, Jun Liu, Jieping Ye

  • 1Department of Computer Science and Engineering and the Center for Evolutionary Medicine and Informatics of the Biodesign Institute, Arizona State University, Tempe, AZ 85287, USA. lei.yuan@asu.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|July 23, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient algorithm for overlapping group Lasso, improving feature selection for complex datasets. The new method is faster and more effective, especially for biological data with overlapping gene sets.

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Published on: October 11, 2018

Area of Science:

  • Statistics
  • Machine Learning
  • Bioinformatics

Background:

  • The standard Group Lasso method is limited by its requirement for non-overlapping feature groups.
  • Overlapping groups present significant optimization challenges in feature selection.
  • Existing methods struggle with the computational complexity of overlapping group structures.

Purpose of the Study:

  • To develop an efficient optimization algorithm for the overlapping group Lasso penalized problem.
  • To generalize the method for overlapping group Lasso formulations using the l(q) norm.
  • To extend the algorithm to a non-convex formulation for reduced estimation bias.

Main Methods:

  • Characterizing the proximal operator of the overlapping group Lasso.
  • Solving the dual problem to compute the proximal operator efficiently.
  • Utilizing gradient descent for optimization.
  • Extending to l(q) norm and capped norm regularizations.

Main Results:

  • The proposed algorithm demonstrates superior efficiency compared to existing state-of-the-art methods.
  • Empirical evaluations on synthetic and breast cancer gene expression data validate the algorithm's performance.
  • The non-convex formulation effectively reduces estimation bias.

Conclusions:

  • The developed optimization technique provides an efficient solution for overlapping group Lasso.
  • The generalized and non-convex formulations offer enhanced flexibility and accuracy in feature selection.
  • The algorithm shows significant promise for applications in high-dimensional data analysis, particularly in genomics.