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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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An efficient voting algorithm for finding additive biclusters with random background.

Jing Xiao¹, Lusheng Wang, Xiaowen Liu

¹Department of Computer Science and Technology, Tsinghua University, Beijing, China.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|December 2, 2008

Summary

This study introduces a voting-based algorithm to efficiently find implanted additive biclusters in large datasets. The algorithm achieves high accuracy, even with overlapping biclusters, making it valuable for computational biology and data mining.

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Area of Science:

Computational Biology
Data Mining
Machine Learning

Background:

Biclustering aims to find optimal subsets of rows and columns in a matrix.
The problem is NP-hard for many objective functions.
This study focuses on a probabilistic model for implanted additive biclusters.

Purpose of the Study:

To develop an efficient algorithm for detecting implanted additive biclusters.
To analyze the algorithm's performance and accuracy under specific conditions.

Main Methods:

A voting-based algorithm with O(n^2m) time complexity is proposed.
The algorithm's correctness is analyzed for bicluster sizes k >= Omega(sqrt(n log n)).
The algorithm is implemented in C++ as the VOTE program.

Main Results:

The voting algorithm correctly identifies the implanted bicluster with probability at least 1 - (9/n^2).
The VOTE program demonstrates high accuracy and speed on simulated and real datasets.
The implementation includes strategies for handling bicluster size estimation, threshold adjustment, and overlapping biclusters.

Conclusions:

The proposed voting algorithm is effective for solving the implanted additive bicluster problem.
The VOTE implementation offers a practical and efficient solution for bicluster detection.
The algorithm shows promise for applications in computational biology and other data-intensive fields.