Logic Regression is an adaptive regression methodology mainly developed to explore highorder interactions in genomic data. Logic Regression is intended for situations where most of the covariates in the data to be analyzed are binary. The goal of Logic Regression is to find predictors that are Boolean (logical) combinations of the original predictors. In this article, we give an overview of the...

MOTIVATION Highly sensitive and specific screening tools may reduce disease -related mortality by enabling physicians to diagnose diseases in asymptomatic patients or at-risk individuals. Diagnostic tests based on multiple biomarkers may achieve the needed sensitivity and specificity to realize this clinical gain. RESULTS Logic regression, a multivariable regression method predicting an outco...

In recent years, much attention has been focused on the ability of fuzzy logic to solve problems that were previously assumed difficult to solve. These include, for example, the use of fuzzy logic for control and pattern recognition. However, the application of fuzzy logic to the establishment of correlations between independent and dependent process variables, i.e. regression modeling, has rec...

coastal lagoons are complex ecosystems exhibiting a high degree of non-linearity in the distribution and exchange of nutrients dissolved in the water column due to their spatio-temporal characteristics. this factor has a direct influence on the concentrations of chlorophyll-a, an indicator of the primary productivity in the water bodies as lakes and lagoons. moreover the seasonal variability in...

Automated production systems are usually driven by Programmable Logic Controllers (PLCs). These systems are long-living – yet have to adapt to changing requirements over time. This paper presents a novel method for regression verification of PLC code, which allows one to prove that a new revision of the plant’s software does not break existing intended behavior. Our main contribution is the des...