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Abstract: Abstract: Kidney allograft rejection is one of the main limitations to long-term kidney transplant survival. The diagnostic gold standard for detecting rejection is a kidney biopsy, an invasive procedure that can often give imprecise results due to complex diagnostic criteria and high interobserver variability. In recent years, several additional diagnostic approaches to rejection have been investigated, some of them with the aid of machine learning (ML). In this review, we addressed studies that investigated the detection of kidney allograft rejection over the last decade using various ML algorithms. Various ML techniques were used in three main categories: (a) histopathologic assessment of kidney tissue with the aim to improve the diagnostic accuracy of a kidney biopsy, (b) assessment of gene expression in rejected kidney tissue or peripheral blood and the development of diagnostic classifiers based on these data, (c) radiologic assessment of kidney tissue using diffusion-weighted magnetic resonance imaging and the construction of a computer-aided diagnostic system. In histopathology, ML algorithms could serve as a support to the pathologist to avoid misclassifications and overcome interobserver variability. Diagnostic platforms based on biopsy-based transcripts serve as a supplement to a kidney biopsy, especially in cases where histopathologic diagnosis is inconclusive. ML models based on radiologic evaluation or gene signature in peripheral blood may be useful in cases where kidney biopsy is contraindicated in addition to other non-invasive biomarkers. The implementation of ML-based diagnostic methods is usually slow and undertaken with caution considering ethical and legal issues. In summary, the approach to the diagnosis of rejection should be individualized and based on all available diagnostic tools (including ML-based), leaving the responsibility for over- and under-treatment in the hands of the clinician.
Keywords: kidney transplantation, rejection, diagnosis, machine learning, kidney biopsy
Published in DiRROS: 08.12.2025; Views: 49; Downloads: 23
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Abstract: Foamed glass is a lightweight material commonly used for insulation. However, optimizing its properties remains a challenge due to the large number of synthesis parameters involved in its production. While previous studies have investigated synthesis conditions, a comprehensive study applying machine learning approaches is lacking in the literature. In this paper, we apply machine learning methods, i.e., random forests of predictive clustering trees and a multilayer perceptron, training them on 124 experimental data points to accurately predict the apparent density and closed porosity of foamed glass. We then apply a multiobjective optimization algorithm together with the multilayer perceptron to find optimal values for the process parameters used in foamed glass production. Our results show that the combination of machine learning and multiobjective optimization is an effective proxy for the development of novel foamed glass materials.
Keywords: process optimization, machine learning, foamed glass
Published in DiRROS: 18.11.2025; Views: 172; Downloads: 70
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Abstract: Study region: Slovenia Study focus: This study investigates the relationship between the hydrological functioning of karst aquifer systems and the geomorphological characteristics of their catchments. It is based on the analysis of discharge time series from 15 karst springs. Hydrograph analysis of these time series was used to estimate 11 hydrological parameters that characterize aquifer system functioning. A spatial analysis of morphological, geological, and hydrological data was carried out to assess 7 lumped geomorphological features of the catchments. These features (independent variables) and hydrological parameters (dependent variables) were used to develop random forest models for predicting the hydrological functioning of karst springs. New hydrological insights for the region: The developed methodological approach provides a basis for improved characterization and prediction of the hydrological functioning of ungauged karst systems. Groundwater availability in these systems is largely controlled by aquifer retention capacity and spring discharge variability. These characteristics can be inferred from hydrological parameters that can be predicted using the developed random forest models. Feature importance analysis indicated that catchment area, cave density, and slope gradient are the most important geomorphological features for predicting the hydrological characteristics of spring discharge.
Keywords: karst aquifer, random forest, machine learning, ungauged catchment, spring discharge, recession curve analysis
Published in DiRROS: 22.10.2025; Views: 180; Downloads: 112
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Abstract: Satellite observations of sea surface temperature (SST) are essential for accurate weather forecasting and climate modeling. However, these data often suffer from incomplete coverage due to cloud obstruction and limited satellite swath width, which requires development of dense reconstruction algorithms. The current state of the art struggles to accurately recover high-frequency variability, particularly in SST gradients in ocean fronts, eddies, and filaments, which are crucial for downstream processing and predictive tasks. To address this challenge, we propose a novel two-stage method CRITER (Coarse Reconstruction with ITerative Refinement Network), which consists of two stages. First, it reconstructs low-frequency SST components utilizing a Vision Transformer-based model, leveraging global spatio-temporal correlations in the available observations. Second, a UNet type of network iteratively refines the estimate by recovering high-frequency details. Extensive analysis on datasets from the Mediterranean, Adriatic, and Atlantic seas demonstrates CRITER's superior performance over the current state of the art. Specifically, CRITER achieves up to 44 % lower reconstruction errors of the missing values and over 80 % lower reconstruction errors of the observed values compared to the state of the art.
Keywords: deep learning, reconstruction algorithms, satellite measurements
Published in DiRROS: 14.10.2025; Views: 238; Downloads: 109
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Abstract: The study presented in this paper evaluated gene expression profiles from The Cancer Genome Atlas (TCGA). To reduce complexity, we focused on genes in the cGAS–STING pathway, crucial for cytosolic DNA detection and immune response. The study analyzes three clinical variables: disease-specific survival (DSS), overall survival (OS), and tumor stage. To effectively utilize the high-dimensional gene expression data, we needed to find a way to project these data meaningfully. Since gene pathways can be represented as graphs, a novel method of presenting genomics data using graph data structure was employed, rather than the conventional tabular format. To leverage the gene expression data represented as graphs, we utilized a graph convolutional network (GCN) machine learning model in conjunction with the genetic algorithm optimization technique. This allowed for obtaining an optimal graph representation topology and capturing important activations within the pathway for each use case, enabling a more insightful analysis of the cGAS–STING pathway and its activations across different cancer types and clinical variables. To tackle the problem of unexplainable AI, graph visualization alongside the integrated gradients method was employed to explain the GCN model’s decision-making process, identifying key nodes (genes) in the cGAS–STING pathway. This approach revealed distinct molecular mechanisms, enhancing interpretability. This study demonstrates the potential of GCNs combined with explainable AI to analyze gene expression, providing insights into cancer progression. Further research with more data is needed to validate these findings.
Keywords: cGAS–STING, graph-convolutional-network, graphs, cancer, pan-cancer, machine learning, NGS
Published in DiRROS: 09.09.2025; Views: 294; Downloads: 141
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