THE EVOLVING IDEA OF “HEALTH” IN THE LEGAL LANDSCAPE: AN ATTEMPT TO IDENTIFY CRITICAL ASPECTS

The aim: The overall purpose of the research is to shed light on the evolving understanding of the concept of “health” in the legal field, attempting to identify its critical aspects. Materials and methods: The study constitutes a comprehensive analysis of a large number of scholarly publications and international legal provisions through the prism of the principles of concreteness and objectivity, coupled with methodological pluralism, including both philosophical, general scientific, theoretical, general, and special legal research methods. Conclusions: Considering the evolving idea of “health” in the legal landscape, it is noted that, in the current context, “health” should be perceived both as an individual and a collective feature, as well as the substance, legal right, and a state’s obligation.

Problems of Common Property Management in the Housing Sector

The main specifics of the implementation of the right of common property today is determined by the collective nature of the creation and sale of property, and not by the feature of the property subject (divisibility or indivisibility of property). Accordingly, in order to implement the collective feature of common property, people shall unite, creating self-government organizations. There are judgments about the organization, which are often identified with the term "system", in the educational and scientific literature. Such an establishment of the system concept is more general than the term of organization. The ideas about the organizations created by people to manage common property are the most complex. Self-government is the main type of activity that allows for the fair distribution of the good and the burden of common property among participants in common property. It is this process that shall be designed and implemented as self-government, so that all conditions are sufficient for the collective to achieve common property and satisfy personal needs of each of them using common power. As a result, it is necessary to create a self-government organization to implement common property. The emergence (creation) of such an organization is the first necessary condition for the implementation of common property. If an organization is not created from among all participants in the common property, then the common property cannot be implemented. Thus, in this article, model representations of the simplest self-government organization will be introduced, which are necessary to solve the problems of decision-making and the implementation of common property

Collective feature selection to identify crucial epistatic variants

BackgroundMachine learning methods have gained popularity and practicality in identifying linear and non-linear effects of variants associated with complex disease/traits. Detection of epistatic interactions still remains a challenge due to the large number of features and relatively small sample size as input, thus leading to the so-called “short fat data” problem. The efficiency of machine learning methods can be increased by limiting the number of input features. Thus, it is very important to perform variable selection before searching for epistasis. Many methods have been evaluated and proposed to perform feature selection, but no single method works best in all scenarios. We demonstrate this by conducting two separate simulation analyses to evaluate the proposed collective feature selection approach.ResultsThrough our simulation study we propose a collective feature selection approach to select features that are in the “union” of the best performing methods. We explored various parametric, non-parametric, and data mining approaches to perform feature selection. We choose our top performing methods to select the union of the resulting variables based on a user-defined percentage of variants selected from each method to take to downstream analysis. Our simulation analysis shows that non-parametric data mining approaches, such as MDR, may work best under one simulation criteria for the high effect size (penetrance) datasets, while non-parametric methods designed for feature selection, such as Ranger and Gradient boosting, work best under other simulation criteria. Thus, using a collective approach proves to be more beneficial for selecting variables with epistatic effects also in low effect size datasets and different genetic architectures. Following this, we applied our proposed collective feature selection approach to select the top 1% of variables to identify potential interacting variables associated with Body Mass Index (BMI) in ~44,000 samples obtained from Geisinger’s MyCode Community Health Initiative (on behalf of DiscovEHR collaboration).ConclusionsIn this study, we were able to show that selecting variables using a collective feature selection approach could help in selecting true positive epistatic variables more frequently than applying any single method for feature selection via simulation studies. We were able to demonstrate the effectiveness of collective feature selection along with a comparison of many methods in our simulation analysis. We also applied our method to identify non-linear networks associated with obesity.