ArticlePloS one2016
A New Algorithm to Optimize Maximal Information Coefficient.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Statistical Methods for Detecting Nonlinear Relationships in Gene Expression and Omics Data: A Review.International journal of molecular sciences · 2026Review
- Dynamic brain-heart-gut coupling during sleep: a continuous physiological signal analysis.Frontiers in neuroscience · 2025Article
- An efficient, not-only-linear correlation coefficient based on clustering.Cell systems · 2024Article
- iSuc-ChiDT: a computational method for identifying succinylation sites using statistical difference table encoding and the chi-square decision table classifier.BioData mining · 2022Article
- An improved algorithm for the maximal information coefficient and its application.Royal Society open science · 2021Article
- Chi-MIC-share: a new feature selection algorithm for quantitative structure-activity relationship models.RSC advances · 2020Article
- A high-performance approach for predicting donor splice sites based on short window size and imbalanced large samples.Biology direct · 2019Article
- A Novel Method to Efficiently Highlight Nonlinearly Expressed Genes.Frontiers in genetics · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
The maximal information coefficient (MIC) captures dependences between paired variables, including both functional and non-functional relationships. In this paper, we develop a new method, ChiMIC, to calculate the MIC values. The ChiMIC algorithm uses the chi-square test to terminate grid optimization and then removes the restriction of maximal grid size limitation of original ApproxMaxMI algorithm. Computational experiments show that ChiMIC algorithm can maintain same MIC values for noiseless functional relationships, but gives much smaller MIC values for independent variables. For noise functional relationship, the ChiMIC algorithm can reach the optimal partition much faster. Furthermore, the MCN values based on MIC calculated by ChiMIC can capture the complexity of functional relationships in a better way, and the statistical powers of MIC calculated by ChiMIC are higher than those calculated by ApproxMaxMI. Moreover, the computational costs of ChiMIC are much less than those of ApproxMaxMI. We apply the MIC values tofeature selection and obtain better classification accuracy using features selected by the MIC values from ChiMIC.
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