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Efforts of the scientific community led to the development of multiple screening approaches for COVID-19 that rely on machine learning methods. However, there is a lack of works showing how to tune the classification models used for such a task and what the tuning effect is in terms of various classification quality measures. Understanding the impact of classifier tuning on the results obtained will allow the users to apply the provided tools consciously. Therefore, using a given screening test they will be able to choose the threshold value characterising the classifier that gives, for example, an acceptable balance between sensitivity and specificity. The presented work introduces the optimisation approach and the resulting classifiers obtained for various quality threshold assumptions. As a result of the research, an online service was created that makes the obtained models available and enables the verification of various solutions for different threshold values on new data. [ABSTRACT FROM AUTHOR]

Summary Patient multi-omics datasets are often characterized by a high dimensionality; however, usually only a small fraction of the features is informative, that is change in their value is directly related to the disease outcome or patient survival. In medical sciences, in addition to a robust feature selection procedure, the ability to discover human-readable patterns in the analyzed data is also desirable. To address this need, we created MAINE—Multi-omics Analysis and Exploration. The unique functionality of MAINE is the ability to discover multidimensional dependencies between the selected multi-omics features and event outcome prediction as well as patient survival probability. Learned patterns are visualized in the form of interpretable decision/survival trees and rules. Availability and implementation MAINE is freely available at maine.ibemag.pl as an online web application. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]