Accurate residential property valuation is essential for mortgage lending, taxation, and urban planning, yet remains challenging due to complex spatial and temporal dynamics. Traditional econometric models are interpretable but rely on restrictive assumptions, while machine learning–based automated valuation models (AVMs) improve predictive accuracy but treat transactions as independent, overlooking spatial spillovers and evolving trends. Recent graph-based approaches partially address spatial autocorrelation, but often rely on static or unidirectional structures that limit their expressiveness.

We introduce a Multi-Scale Bidirectional Spatio-Temporal Graph Neural Network (MBSTGNN) that models transactions and neighbourhoods as dynamic graphs linked through bidirectional message passing. A temporal memory mechanism maintains consistency across time, enabling the model to capture evolving market conditions. Evaluated on Rotterdam housing transactions, MBSTGNN outperforms strong baselines, particularly in sparse-data settings, and produces embeddings that reveal domain-consistent socio-spatial and temporal patterns. These results demonstrate its potential for advancing automated valuation and related spatio-temporal prediction tasks.

Type 2 Diabetes is a very prevalent disease in current times and leads to significant adverse effects. Recently, there has been a growing interest in the association of the human gut microbiome with respect to chronic diseases like Type 2 Diabetes with the aim to identify biomarkers. In this study, we researched the effect of different machine learning and feature selection techniques to identify biomarkers for Type 2 Diabetes that can later be used for diagnosis and prediction. The main methods that we explored were Random Forests,Linear Regression, Support Vector Machines andXGBoost along with mRMR and CMIM as feature selection techniques. These methods were applied to data taken from Europe and China. We found that mRMR improved the performance of the Random Forest classifier compared to CMIM.Apart from finding biomarkers specific to one location, we found that Clostridiales, Clostridium, Roseburia and Lactobacillus could be of interestin the prediction of Type 2 Diabetes irrespective of location. This study verified biomarkers found in previous literature and evaluated several techniquesfor the prediction of the disease across different regions.