BACKGROUND: Identifying relevant biomarkers is critical in clinical research and precision medicine, particularly when analysing high-dimensional data. Random forests (RFs) are promising for such settings due to their flexibility, ease of use, and their ability to handle data sets with more variables than samples. RFs assess the importance of each variable in predicting the outcome using variable importance (VIMP) scores. However, since the distribution of VIMP scores is intricate, standard statistical testing and multiple testing adjustments for variable selection are challenging. METHODS: We propose shadowVIMP, a novel method for multiple testing-controlled variable selection, based on an approach similar to permutation testing. It generates permuted counterparts for each variable and compares their VIMPs with those of the original variables over multiple iterations to calculate p-values. Unlike conventional permutation testing, shadowVIMP preserves the correlation structure between variables, mitigating biases caused by the over-selection of correlated variables in RFs. We evaluated shadowVIMP against three competing RF variable selection approaches using simulation designs previously employed in studies considering VIMPs and variable selection for RFs. These designs included high- and low-dimensional data, as well as correlated and categorical variables. For illustration, we also applied the method to a real-world example on Alzheimer's disease. CONCLUSIONS: Our results showed that, compared to competing approaches, shadowVIMP offers advantages in high-dimensional settings, improving sensitivity while enabling multiple testing-adjusted results. Additionally, it demonstrated robustness against VIMP biases induced by correlated and categorical variables when using permutation-based VIMP. The method can be used to annotate standard VIMP plots, visually presenting selected variable sets based on different types of multiple testing adjustments and significance levels. Overall, shadowVIMP is a promising approach for providing multiple testing-adjusted variable selection while explicitly addressing known biases of RF's permutation-based VIMP measure. The shadowVIMP method is implemented in an R package shadowVIMP, which is available on CRAN.

Since its introduction at GPCE2020, the eFLINT norm specification language has been used in academic and industrial applications to specify and automate compliance for various norms, such as privacy regulations and data processing agreements. The eFLINT interpreter has been used to automate the analysis of real-time or historical cases by computing logical consequences and reporting normative violations.

We report on the ideas and experiences of adapting Brane, a workflow execution framework, for use cases involving medical data exchange and processing. These use cases impose new requirements on the system to enforce policies encoding safety properties, ranging from access control to legal regulations pertaining to data privacy. Our approach emphasizes users' control over the extent to which they cooperate in distributed execution, at the cost of revealing information about their policies.

On the road towards personalised medicine, one of the main challenges is to enforce security and network low-level policies to secure data-sharing. The proposed dynamic framework defines the topology of the service chains to enforce network and security policies by instantiating Virtual Network Functions (VNF's) on the fly via light-weight and easily-deployable containers. In this paper, we profile the resource utilisation of chained VNF's deployed to enable data movement within different healthcare use cases. We provide example configurations that map to a couple of use cases (e-Health record query and heath data streaming), then we monitor and collect CPU utilisation of the different VNF compositions. In the considered policies we can: discard flow, protect (encrypt) and transmit, or allow with no protection. To enforce each policy, we deploy a firewall function (relatively heavy-weight function), encryption function, and a decryption function (light-weight stream cipher). As a result, we analyse the behaviour of the VNF services with various setups, and then we aim to further use this analysis to build the placement heuristic according to available and trusted clusters resources. Subsequently, we will recommend heuristic-based placement based on collected profiling data of the resource usage and limits for high availability, optimal performance, and minimal resource waste.