The maximal information coefficient (MIC) captures both linear and nonlinear correlations
between variable pairs. In this paper, we proposed the BackMIC algorithm for MIC …

The supplementary material consists of four sections. In Section 1 of the supplementary
material we give some more details of Section 4.2 of the main article concerning the …

Identifying meaningful patterns in data is crucial for understanding complex biological
processes, particularly in transcriptomics, where genes with correlated expression often …

The drag force model is vital for capturing gas–solid flow dynamics in many simulation
approaches. Most of the homogeneous drag models in the literature are expressed as a …

As the main source of ship features, ship-radiated noise plays a key role in recognizing
different types of ships. Therefore, to effectively extract the features from ship-radiated noise …

High-dimensional data have been a challenging problem in classification. Feature selection
works as a filter to remove irrelevant or redundant features and has made comparative …

Emerging modern data analytics attracts much attention in materials research and shows
great potential for enabling data-driven design. Data populated from the high-throughput …

Since the decision trees (DTs) have an advantage over “black-box” models, such as neural
nets or support vector machines, in terms of comprehensibility, such that it might merit …

In musculoskeletal systems, describing accurately the coupling direction and intensity
between physiological electrical signals is crucial. The maximum information coefficient …

Correlation coefficients are widely used to identify patterns in data that may be of particular
interest. In transcriptomics, genes with correlated expression often share functions or are …