Network Models of BACE-1 Inhibitors: Exploring Structural and Biochemical Relationships.

This study investigates the clustering patterns of human ß-secretase 1 (BACE-1) inhibitors using complex network methodologies based on various distance functions, including Euclidean, Tanimoto, Hamming, and Levenshtein distances. Molecular descriptor vectors such as molecular mass, Merck Molecular Force Field (MMFF) energy, Crippen partition coefficient (ClogP), Crippen molar refractivity (MR), eccentricity, Kappa indices, Synthetic Accessibility Score, Topological Polar Surface Area (TPSA), and 2D/3D autocorrelation entropies are employed to capture the diverse properties of these inhibitors. The Euclidean distance network demonstrates the most reliable clustering results, with strong agreement metrics and minimal information loss, indicating its robustness in capturing essential structural and physicochemical properties. Tanimoto and Hamming distance networks yield valuable clustering outcomes, albeit with moderate performance, while the Levenshtein distance network shows significant discrepancies. The analysis of eigenvector centrality across different networks identifies key inhibitors acting as hubs, which are likely critical in biochemical pathways. Community detection results highlight distinct clustering patterns, with well-defined communities providing insights into the functional and structural groupings of BACE-1 inhibitors. The study also conducts non-parametric tests, revealing significant differences in molecular descriptors, validating the clustering methodology. Despite its limitations, including reliance on specific descriptors and computational complexity, this study offers a comprehensive framework for understanding molecular interactions and guiding therapeutic interventions. Future research could integrate additional descriptors, advanced machine learning techniques, and dynamic network analysis to enhance clustering accuracy and applicability.

Empirical investigation on how wellbeing-related infrastructure shapes economic growth: Evidence from the European Union regions.

One of the most important policies of the European Union is regional development, which comprises measures of enhancing economic growth and citizens' living standards via strategic investment. Considering that economic growth and wellbeing are intertwined from the perspective of EU policies, this study examines the relationship between wellbeing-related infrastructure and economic growth in 212 NUTS 2 regional subdivisions across the members of Eu-28 during the period 2001-2020. We therefore analyzed data from 151 Western Europe regions and 61 Central and Eastern Europe regions by means of a panel data analysis with the first-difference generalized method of moments estimator. Our main interest was to determine the degree to which Western Europe regions responded to predictors as compared to Central and Eastern Europe regions. According to the empirical results, the predictors with the strongest influence for Western Europe regions were disposable household income, inter-regional mobility, housing indicator, labor force and participation. For Central and Eastern Europe regions, the largest impact was triggered by the housing indicator, internet broadband access and air pollution. In addition, we determined a relational weighted multiplex between all variables of interest by using dynamic time warping and we introduced topological measures in a multilayer multiplex model for both regional subsamples.

Discrete Geodesic Distribution-Based Graph Kernel for 3D Point Clouds.

In the structural analysis of discrete geometric data, graph kernels have a great track record of performance. Using graph kernel functions provides two significant advantages. First, a graph kernel is capable of preserving the graph's topological structures by describing graph properties in a high-dimensional space. Second, graph kernels allow the application of machine learning methods to vector data that are rapidly evolving into graphs. In this paper, the unique kernel function for similarity determination procedures of point cloud data structures, which are crucial for several applications, is formulated. This function is determined by the proximity of the geodesic route distributions in graphs reflecting the discrete geometry underlying the point cloud. This research demonstrates the efficiency of this unique kernel for similarity measures and the categorization of point clouds.

A Series-Based Deep Learning Approach to Lung Nodule Image Classification.

Although many studies have shown that deep learning approaches yield better results than traditional methods based on manual features, CADs methods still have several limitations. These are due to the diversity in imaging modalities and clinical pathologies. This diversity creates difficulties because of variation and similarities between classes. In this context, the new approach from our study is a hybrid method that performs classifications using both medical image analysis and radial scanning series features. Hence, the areas of interest obtained from images are subjected to a radial scan, with their centers as poles, in order to obtain series. A U-shape convolutional neural network model is then used for the 4D data classification problem. We therefore present a novel approach to the classification of 4D data obtained from lung nodule images. With radial scanning, the eigenvalue of nodule images is captured, and a powerful classification is performed. According to our results, an accuracy of 92.84% was obtained and much more efficient classification scores resulted as compared to recent classifiers.

Hierarchies in communities of UK stock market from the perspective of Brexit.

Nowadays, increase of analyzing stock markets as complex systems lead graph theory to play a key role. For instance, detecting graph communities is an important task in the analysis of stocks, and as planar maximally filtered graphs let us to get important information for the topology of the market. In this study, we first obtain correlation network representation of UK's leading stock market network by using a novel threshold method. Then, we determine vertex clusters by using modularity and analyze clusters in planar maximally filtered graph substructures. Our analyze include a new measure called weighted Gini index for measuring the sparsity. The main goal of this paper is to study the hierarchical evolution of the market communities throughout the Brexit referendum, which is known as the stress period for the stock market. Hence, the overall sample is divided into two sub-periods of pre-referendum, and post-referendum to obtain communities and hierarchical structures. Our results indicate that financial companies are leading elements of the clusters. Moreover, the significant changes within the network topologies are observed for insurance, consumer goods, consumer services, mining, and technology sectors whereas oil and gas and health care sectors have not been affected by Brexit stress.