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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

The γ-OMP Algorithm for Feature Selection With Application to Gene Expression Data.

Michail Tsagris, Zacharias Papadovasilakis, Kleanthi Lakiotaki

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|October 9, 2020

Summary

This summary is machine-generated.

We introduce gamma-OMP, a scalable feature selection algorithm for predictive analytics. It handles diverse data types and outperforms LASSO on gene expression datasets for classification and regression tasks.

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Area of Science:

Bioinformatics
Computational Biology
Statistical Learning

Background:

Feature selection is crucial for predictive analytics, especially with high-dimensional molecular data.
Existing algorithms like LASSO face scalability challenges with tens of thousands of features.

Purpose of the Study:

To develop a generalized and scalable feature selection algorithm, gamma-OMP.
To enhance predictive analytics for diverse molecular data types.

Main Methods:

Proposed gamma-OMP, a generalization of Orthogonal Matching Pursuit.
Enabled handling of various outcomes (continuous, binary, time-to-event), categorical features, and predictive models.
Compared gamma-OMP against LASSO using simulated and real gene expression datasets.

Main Results:

Gamma-OMP demonstrated comparable or superior performance to LASSO.
Effectiveness shown across binary classification, regression, and time-to-event analyses.
Successful application in bioinformatics analysis settings.

Conclusions:

Gamma-OMP offers a scalable and versatile solution for feature selection in high-dimensional data.
The algorithm's flexibility and effectiveness make it suitable for complex bioinformatics tasks.
Gamma-OMP is easy to implement and extend for future research.