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An improved algorithm for the maximal information coefficient and its application.

Dan Cao1,2, Yuan Chen1, Jin Chen1

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|May 11, 2021
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Summary
This summary is machine-generated.

The new BackMIC algorithm improves maximal information coefficient (MIC) estimation by enhancing grid partitioning and using a chi-squared test for termination. This leads to more accurate correlation measurements and better clustering of cancer data.

Keywords:
K-means clusteringequitabilitymaximal information coefficientstatistical powerχ2-test

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Area of Science:

  • Statistics
  • Bioinformatics
  • Machine Learning

Background:

  • Maximal Information Coefficient (MIC) is a powerful metric for capturing complex relationships between variables.
  • Existing MIC estimation algorithms like ApproxMaxMI have limitations in grid partitioning and termination criteria.
  • Accurate correlation estimation is crucial for various data analysis tasks, including biological data clustering.

Purpose of the Study:

  • To introduce the BackMIC algorithm for improved Maximal Information Coefficient (MIC) estimation.
  • To enhance grid partitioning and introduce a statistically-grounded termination criterion for MIC calculation.
  • To evaluate the performance of BackMIC in terms of accuracy, robustness, and application in biological data clustering.

Main Methods:

  • Developed the BackMIC algorithm incorporating a backward searching process for optimized grid partitioning.
  • Implemented a chi-squared test for adaptive termination of the grid search, replacing fixed bin limits.
  • Applied the (1-MIC) distance metric derived from BackMIC in K-means clustering for cancer/normal sample analysis.

Main Results:

  • BackMIC provides more reasonable grid partitions and MIC values compared to ApproxMaxMI, maintaining MIC's generality.
  • The algorithm demonstrates robustness across different alpha values and improves statistical power and equitability.
  • Clustering analysis on four cancer datasets using BackMIC-derived distances yielded superior results compared to other methods.

Conclusions:

  • The BackMIC algorithm offers a significant advancement in MIC estimation, providing more credible correlation measurements.
  • Its improved accuracy and robustness make it a valuable tool for statistical analysis and machine learning applications.
  • BackMIC enhances the reliability of correlation-based clustering, particularly in complex biological datasets.