MOTIVATION: DNA methylation (DNAme) is the best studied epigenetic mechanism that plays pivotal role in tissue differentiation and epigenetic disruption has been correlated to diverse disease types (e.g. cancer, metabolic disorders). While various DNAme array platforms have been discovered, data analysis remains a challenging task which often requires in-depth bioinformatic expertise. Here, we developed a user-friendly web-based application for data analysis and visualization that accommodates users ranging from early-career basic/translational researchers to experienced bioinformaticians. RESULTS: CpGene is a web application for analyzing DNA methylation array data. It supports Illumina 450K, EPIC, and EPICv2 methylation array platforms and processes .idat files with integrated preprocessing, normalization, and quality control. Biomarker discovery is available through either classic differential methylation point analysis or machine learning-based feature selection as well as gene enrichment analysis. Results are summarized with clear visualizations, to aid interpretation. By combining these functions in a unified interface, CpGene streamlines methylation analysis and helps identify CpG sites and genes with biological and clinical relevance. AVAILABILITY AND IMPLEMENTATION: CpGene is openly accessible as a web service through http://cpgene.duckdns.org:8001/ and it's source code is available on https://github.com/kostaslazaros/cpgenene.

The growing complexity and velocity of immune checkpoint blockage (ICB) research demand intelligent, scalable systems capable of parsing vast biomedical corpora, synthesizing cross-study findings, and producing explainable outputs for clinical and scientific decision-making. This paper introduces SwarmICB, a multi-agent AI system architected specifically for ICB literature exploration and biomolecular analysis. SwarmICB comprises two coordinated agent swarms-one for publication processing and structured data extraction, and another for knowledge synthesis and real-time question answering. Powered by domain-specialized prompts, retrieval-augmented generation (RAG), and integration with external bioinformatics tools through the Model Context Protocol (MCP), the system enables high-throughput, explainable reasoning over validated biomedical knowledge. The User Interface provides real-time interactivity and visual feedback, facilitating seamless access to literature insights, protein structures, and emerging immunotherapy trends. The platform is evaluated through a complete end-to-end use case on PD-1 (PDCD1), demonstrating capabilities in multi-database retrieval, biological interpretation, and AI-assisted synthesis. SwarmICB presents a scalable, agentic architecture for automated literature intelligence in immuno-oncology, offering a blueprint for next-generation biomedical knowledge systems.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder primarily linked to memory loss and cognitive decline, although less common clinical manifestations are being more frequently identified. Disruptions in gene expression and regulation have been increasingly linked to neurodegeneration. Single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) are powerful methods for analyzing transcriptomic diversity at the level of individual cells. Herein, analysis of snRNA-seq data derived from the entorhinal cortex of post-mortem brain samples from individuals either diagnosed with AD (Braak stage II) or serving as healthy controls was executed trying to identify potent biomarkers and molecular mechanisms in which these genes are involved. Preprocessing, integration, clustering and cell type annotation were performed using indicator-based methods followed by differentially expression analysis and enrichment analysis. UMAP was used for data visualization as well as Local Inverse Simpson's Index to evaluate the effectiveness of data integration. Leiden clustering was performed on the integrated dataset while the Wilcoxon rank-sum test was utilized for differential expression analysis. Cell-cell junctions were detected as the most significantly enriched category showing significant contribution to the structural integrity and signaling across the blood-brain barrier. Moreover, important pathways such as the Brain-Derived Neurotrophic Factor and Lglutamate transmembrane transport were detected emphasizing the role of synaptic dysfunction and neurodegeneration in AD. This approach supports the implementation of snRNA-seq data analysis in complex disorders such as neurodegenerative diseases, providing new challenges for identifying fundamental biological processes on a molecular perspective.

Alzheimer’s Disease (AD) remains a formidable challenge in neurodegenerative research, necessitating innovative approaches to uncover novel therapeutic strategies. This study presents a graph-based approach to integrate large-scale drug and protein data, aiming to identify potential drug repurposing candidates for AD. Our methodology constructs a comprehensive graph incorporating protein-protein interactions and drug-protein relations, providing a multifaceted view of the intricate relationships within the biological and pharmacological landscape. By leveraging this graph, we conduct an in-depth analysis to explore various drug repurposing possibilities, focusing on the alignment of AD-related single-cell transcriptomic data. Our approach enables the identification of promising drug candidates by examining the connectivity and interaction patterns within the graph, revealing potential therapeutic targets and drug synergies that may be beneficial for AD treatment. The integration of diverse data types allows for a more holistic understanding of the underlying molecular mechanisms and drug interactions. Through this graph-based analysis, we uncover several promising drug repurposing candidates, providing a foundation for further experimental validation and clinical investigation. This study underscores the potential of leveraging large-scale data and graph-based methodologies in drug repurposing efforts for neurodegenerative diseases, contributing to the advancement of therapeutic research in AD. Our findings illuminate the importance of integrative data analysis in biomedical research, paving the way for the development of more effective and targeted therapeutic interventions for AD.

Predicting the three-dimensional structure of proteins directly from their sequence of amino acids remains a challenge in biomedical research. Protein functionality depends not only on its sequence but also on the precise folding that occurs during the process of developing their tertiary structure. Misfolded proteins may lead to the generation of entities that are inherently toxic to the organism, such as the formation of amyloid fibrils in the context of Alzheimer’s disease. Herein, the structural conformation of specific missense mutations in proteins involved in Alzheimer’s disease was performed through computational analysis and prediction of the binding mode and multiplicity of them was further assessed. Our findings reveal direct sequence-to-structure motifs from single polypeptides and the received domains with the proper fold. Nevertheless, the positions with the particular deviations are most commonly accompanied by limited downward spikes in pLDDT value, suggesting lower prediction confidence and potential disorder.

One challenge facing Systems Biology is the conversion of vast amounts of data into systematic and organized knowledge through automated processes. Improvements in experimental technologies have created an enormous pool of heterogeneous omics data such as genomics, proteomics and metabolomics. Exporting insights of these datasets can lead to important discoveries for complex pathologies such as age-related neurodegenerative disorders and specifically Parkinson's disease (PD). However, such data are characterized by huge dimensionality which increases their complexity for various data analyses as well for data mining processes such as clustering and classification. In this perspective, we implemented state-of-the-art Dimensionality Reduction Techniques for Visualizing Omics High-Dimensional Parkinson’s Disease Data. Our study highlights the cutting-edge dimensionality reduction techniques for 2D data visualization and their contribution the deeper interpretation of PD data. Approaches in this direction can provide a deeper understanding of biological dynamics and enable integrative multilayered diagnostic assessment of complex disorders such as neurodegenerative diseases.

This poster will present results from an EEG study comparing visual and textual programming, carried out on informatics students. We observe brain rhythms as the subjects complete programming tasks using Python and Scratch. The present study belongs in the domain of Neuroeducation/Educational Neuroscience, which is an attempt to join cognitive science and education in order to create a sound grounding of education. Our goal is to gain a better understanding of different types of programming as one of the parameters that can impact professional involvement in software development, so as to help us formulate educational methodologies in software engineering education.

BACKGROUND: Although the etiology of chronic lymphocytic leukemia (CLL), the most common type of adult leukemia, is still unclear, strong evidence implicates antigen involvement in disease ontogeny and evolution. Primary and 3D structure analysis has been utilised in order to discover indications of antigenic pressure. The latter has been mostly based on the 3D models of the clonotypic B cell receptor immunoglobulin (BcR IG) amino acid sequences. Therefore, their accuracy is directly dependent on the quality of the model construction algorithms and the specific methods used to compare the ensuing models. Thus far, reliable and robust methods that can group the IG 3D models based on their structural characteristics are missing. RESULTS: Here we propose a novel method for clustering a set of proteins based on their 3D structure focusing on 3D structures of BcR IG from a large series of patients with CLL. The method combines techniques from the areas of bioinformatics, 3D object recognition and machine learning. The clustering procedure is based on the extraction of 3D descriptors, encoding various properties of the local and global geometrical structure of the proteins. The descriptors are extracted from aligned pairs of proteins. A combination of individual 3D descriptors is also used as an additional method. The comparison of the automatically generated clusters to manual annotation by experts shows an increased accuracy when using the 3D descriptors compared to plain bioinformatics-based comparison. The accuracy is increased even more when using the combination of 3D descriptors. CONCLUSIONS: The experimental results verify that the use of 3D descriptors commonly used for 3D object recognition can be effectively applied to distinguishing structural differences of proteins. The proposed approach can be applied to provide hints for the existence of structural groups in a large set of unannotated BcR IG protein files in both CLL and, by logical extension, other contexts where it is relevant to characterize BcR IG structural similarity. The method does not present any limitations in application and can be extended to other types of proteins.

The diagnosis of inherited peripheral neuropathies can be a challenging issue in several ways. Current research is focused on a multidisciplinary approach, developing new therapeutic strategies mainly involving online databases and repositories for sharing data and models used in some clinical trials. In this paper authors introduce the general architecture of an automated neural network based model for simulating the prediction procedure of Charcot-Marie-Tooth disease, according to the latest clinical studies.

This article investigates the uses and gratifications of the popular social networking site Facebook. In the exploratory stage, 70 users generated phrases to describe the manner they used Facebook. Interestingly, some users not only described the uses, but also mentioned how they perceive these uses. These phrases were coded into 14 items and clustered into four factors. The principal component analysis that was conducted in the third stage of the study, which was addressed to 222 Facebook users, verified the validity of the four factors: Social Connection, Social Network Surfing, Wasting Time and Using Applications. Previous user studies on Facebook have examined the immediate social effects of this popular social networking site, but they have not regarded emerging uses of the platform, such as gaming and applications, which do have a social component as a feature and not as a core principle. The ‘Wasting Time’ factor and the growth of ‘Using Applications’ factor indicate that Facebook has already become an integral part of daily computing routine, alongside with the rest of the entertainment desktop and web applications.

In this study, we present results of a research that investigates the impact of attending programming courses at Lyceum (15-18 years old students), on students' confidence and behavioral intention towards algorithmic logic. In particular, we measured students' behavioral intention for programming and students' confidence regarding data structures, problem solving and programming commands (Conditional - Loop). Responses from 81 graduate students, whose curriculum included programming courses at Lyceum were used to examine the benefits of programming in secondary education. The results indicate that students with prior attendance of programming courses exhibit high levels of confidence and acceptance regarding structured logic.

In this study, we extended the Unified Theory of Acceptance and Use of Technology (UTAUT) to include key variables from Social Cognitive Theory (SCT) and Theory of Planed Behavior (TPB). We used this hybrid framework to clarify several issues regarding the adoption of the educational webcasts' and to investigate the effects of the key variables. Responses from 292 webcast based learners were used to examine the adoption of the educational webcast.

The aim of the research reported in the paper was twofold: to propose a new approach in cluster analysis and to investigate its performance, when it is combined with dimensionality reduction schemes. The search process for the optimal clusters approximating the unknown classes towards getting homogenous groups, where the homogeneity is defined in terms of the dasiatypicalitypsila of components with respect to the current skeleton. Our method is described in the third section of the paper. The compression scheme was set in terms of the principal directions corresponding to the available cloud. The final section presents the results of the tests aiming the comparison between the performances of our method and the standard k-means clustering technique when they are applied to the initial space as well as to compressed data.