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.

The interaction of tumor cells with their environment and particularly the immune system is increasingly recognized as a key factor of tumorigenesis and tumor progression. However, the expression levels of genes alone cannot capture directly the complexity of these interactions, and a more elaborated framework is necessary. We propose a new cell-cell interactions (CCIs) feature space based on scRNA-Seq data of multiple patient samples to classify cancers into subtypes at the patient level. Specifically, we transform gene expression profiles into a redefined feature space that takes into account interactions between ligands and receptors across all possible pairs of cell types for each patient. To handle the ultrahigh dimensionality arising from the hundreds of thousands of CCIs, we propose an ensemble feature selection algorithm to prioritize the most discriminatory CCIs. Our machine learning pipeline identifies a set of CCIs that accurately classifies cancer samples into subtypes. We apply our computational framework, named pCCI, to the Pancreatic Ductal Adenocarcinoma and Glioblastoma Multiforme subtypes classification, achieving high classification performance. Our work provides compelling evidence that the CCI space can successfully reveal differences in the microenvironment of different cancer subtypes and identify novel pharmacological targets.

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.

In recent years, feature selection has become an increasingly active field of data science and machine learning research. Most of the datasets that are being used nowadays for various machine learning tasks consist of thousands of features (columns), which make them extremely complex and difficult to work with. In this paper, we propose a feature selection methodological pipeline that can be used to reduce the complexity of high dimensional datasets through the elimination of redundant and/or non-informative features as well as to improve the performance of machine learning models which are trained on high dimensional datasets. The proposed method has been applied to high-dimensional biomedical data and compared against a classic filter-based feature selection algorithm. Specifically, the method was applied to gene expression profiles of a single-cell RNA-seq dataset from healthy and infected by covid-19 human samples.

The task of understanding human behavior through intelligent systems is crucial in various domains from medical health and well-being to financial and social platforms. In this work, we propose a complete framework that takes advantage of collected sensor accelerometer data to generate a human activity behavioral model that can be supportive in predicting future development of human movement disabilities such as Osteoarthritis or even in the individual's rehabilitation after a surgery for Osteoarthritis. More precisely, we focus on estimating uncommon behaviors within daily activities as an indication for further examination. Challenge-point of the proposed methodology is the agnostic knowledge of different behaviours of individual's movement. Based on accelerometer sensor data collected from mobile devices, the proposed framework utilizes state-of-the-art Machine Learning models for Human Activity Recognition and introduces new Deep Hybrid Models for outlier detection suggesting a solid basis for further developments and wider applicability.

We explore how Random Projections can be used as an Approximate method for Projection Pursuit Clustering in high dimensional data. Traditional data transformations such as PCA for dimensionality reduction have been shown to be beneficial in clustering. However, their objective is not always relevant to the cluster structure producing undesirable results. On the other hand, Projection Pursuit methods present promising results in finding different "interesting" directions while being easily modified, though they came with high computational costs. In an attempt to provide a lightweight and simplified approach for Projection Pursuit clustering, we designed and implemented the Random Line Approximation Clustering (RLAC), a hierarchical divisive clustering algorithm that incorporates attributes from the Random Projection method.

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.

We are in the era where the Big Data analytics has changed the way of interpreting the various biomedical phenomena, and as the generated data increase, the need for new machine learning methods to handle this evolution grows. An indicative example is the single-cell RNA-seq (scRNA-seq), an emerging DNA sequencing technology with promising capabilities but significant computational challenges due to the large-scaled generated data. Regarding the classification process for scRNA-seq data, an appropriate method is the k Nearest Neighbor (kNN) classifier since it is usually utilized for large-scale prediction tasks due to its simplicity, minimal parameterization, and model-free nature. However, the ultra-high dimensionality that characterizes scRNA-seq impose a computational bottleneck, while prediction power can be affected by the "Curse of Dimensionality". In this work, we proposed the utilization of approximate nearest neighbor search algorithms for the task of kNN classification in scRNA-seq data focusing on a particular methodology tailored for high dimensional data. We argue that even relaxed approximate solutions will not affect the prediction performance significantly. The experimental results confirm the original assumption by offering the potential for broader applicability.

We are in the era of single-cell RNA sequencing technology, which offers a great potential for uncovering cellular differences with a higher resolution, shedding light in various complex biological processes and complex human diseases. However, such studies create extremely high dimensional data isolating expression profiles for thousands or even millions of cells. Consequently, dealing with single-cell RNA-seq (scRNA-seq) data is considered the main challenge for unsupervised clustering, which can be used in order to identify grouped cell types. Towards this direction, we present a framework that enhances hierarchical clustering utilizing Proximity Learning on Random Projected spaces (PLRP). The proposed method's efficiency lies in the fact that we exploit the distances from multiple significantly lower dimension spaces defined by Random Projections using ensembles of k-nearest neighbor searches. In the transformed data we applied hierarchical agglomerative clustering (HAC) improving significantly its performance when compared against using the original space. The performance of the proposed PLRP was evaluated in a publicly available experimental dataset with scRNA-seq expression profiles, against three well-established clustering tools. The results showed that our approach greatly enhances clustering performance exposing its applicability in ultra-high dimensions and imposing further development towards this direction.

We are in the big data era which has affected several domains including biomedicine and healthcare. This revolution driven by the explosion of biomedical data offers the potential for better understanding of biology and human diseases. An illustrative example is the emerging single-cell sequencing technologies, which isolate and measure each cell individually, taking a step beyond the traditional techniques where consider their measurements from a bulk of cell. Although big single-cell RNA sequencing (scRNA-seq) data promises valuable insights into the cellular level, their volume poses several challenges related to the ultra-high dimensionality. Furthermore, to further elucidate the potential of these data, more insight into gene regulatory networks (GRN) is required. Network-based approaches can tackle part of the inherent complexity of human diseases, however, the challenges related to the ultra-high dimensionality are increased. Towards this direction, we propose the NIRP, an algorithm that copes with the high dimensionality of scRNA-data using a workflow based on fast multiple random projections and a radius-based nearest neighbors search. NIRP infers a gene regulatory network (GRN) from big scRNA-seq data by transforming the original data space to a lower dimensions space and capturing the similarities among gene expressions. The network is further analyzed using a random walk approach in order to achieve dense subgraphs, active to the case under study. The performance of NIRP is evaluated in a real single-cell experimental study among three well-established GRN tools. Our results make NIRP a reliable tool, able to handle big single-cell data with ultra-high dimensionality and complexity. he main advantage of this method is that it is not affected by the volume, as much as it increases, since it transforms the data space to a specific low dimensional space.

Single-cell sequencing technologies offer a platform to explore deeper the complex diseases and biological processes. On parallel, Systems Biology approaches can tackle part of this complexity, since they are based on biological networks elucidating genotype to phenotype relationships in a more comprehensive framework. Hence, research community is shifting towards singlecell systems biology approaches to identify and validate disease-specific biomarkers. Under this perspective, we propose a methodological framework aimed to infer disease-specific subpathway activities, using cell signaling pathway topology and transcriptomics data derived from single-cell RNA-seq technologies. More specifically, we concatenated and integrated pathway network refining with single-cell expression profiles. Linear subpathways were extracted and a subpathway profile score was calculated taking into account the pathway information flow. The core of our method is XGBoost supervised classification based on the aforementioned score in order to identify important subpathways that control the rest of the network and cause major changes from a control to a disease state under study. The proposed methodology was applied to a real experimental study with single-cell RNA-seq expression profiles revealing changes in type 2 diabetes. Exported subpathways showed promising results, suggesting that the proposed approach can reliably identify subpathways, acting as potential disease-specific biomarkers.

The recent advent in Next Generation Sequencing has created a huge data source which offers a great potential for elucidating complex disease mechanisms and biological processes. A recent technology is the single-cell RNA sequencing, which allows transcriptomics measurements in individual cells, having promising results. However, such studies measure the entire genome for thousands of cells, creating datasets with extremely high dimensionality and complexity. Following this perspective, we propose a dimensionality reduction approach, called RGt-SNE, which visualizes single-cell RNA-seq data in two dimensions. Initially, RGt-SNE defines a cell-cell distance matrix based on Random Projections and Geodesic Distances. The first is used to define the pairwise cells distances in a low dimensional projected space avoiding the difficulties that exist in data with ultra-high dimensionality. The latter is used to better define the large pairwise cells distances. Subsequently, the t-SNE method is applied in the customized distance matrix for two dimensional visualization. RGt-SNE was evaluated in two real experimental single-cell RNA-seq data against three well-known methods, such as t-SNE, Multidimensional scaling, and ISOMAP. Outcomes provide the superiority of RGt-SNE suggesting it as a reliable tool for single-cell RNA-seq data analysis and visualization.

A major rise in the prevalence and impact of Alzheimer's disease (AD) is projected in the coming decades, resulting from increasing life expectancy, thus leading to substantially increased healthcare costs. While brain disfunctions at the time of diagnosis are irreversible, it is widely accepted that AD pathology develops decades before clinical symptoms onset. If incipient processes can be detected early in the disease progression, prospective intervention for preventing or slowing the disease can be designed. Currently, there is no noninvasive biomarker available to detect and monitor early stages of disease progression. The complex etiology of AD warrants a systems-based approach supporting the integration of multimodal and multilevel data, while network-based modeling provides the scaffolding for methods revealing complex systems-level disruptions initiated by the disease. In this work, we review current state-of-the-art, focusing on network-based biomarkers at molecular and brain functional connectivity levels. Particular emphasis is placed on outlining recent trends, which highlight the functional importance of modular substructures in molecular and connectivity networks and their potential biomarker value. Our perspective is rooted in network medicine and summarizes the pipelines for identifying network-based biomarkers, as well as the benefits of integrating genotype and brain phenotype information for a comprehensively noninvasive approach in the early diagnosis of AD. Finally, we propose a framework for integrating knowledge from molecular and brain connectivity levels, which has the potential to enable noninvasive diagnosis, provide support for monitoring therapies, and help understand heretofore unexamined deep level relations between genotype and brain phenotype.

Biomedicine is undergoing a revolution driven by the explosion of biomedical data, which are generated by emerged medical imaging, sensor technologies and high-throughput technologies. An indicative example is the single cell sequencing technology which concerns the genome sequencing examination of hundreds of separate cells in a single tumor. Consequently, open challenges arising from this emerged technology and generally from the evolution of biomedical technologies under the big data perspective. Also, given the fact that approaches based on high-performance computing require high computing resources and advanced developers, solutions that reduce the problem complexity remain very attractive. Following this direction, in this paper a classification scheme based on Multiple Random Projections and Voting is presented. Random Projections offer a platform not only for a low computational time analysis by significantly reducing the data dimensionality, but also for an accurate analysis which may well exceed classical classification approaches. The proposed method was applied on real biomedical high dimensional data and compared against well-known classification schemes as to Random Projection-based cutting-edge methods. Specifically, we applied it on expression profiles for single-cell RNA-seq data from non-diabetic and type 2 diabetic human samples. Experimental results showed that based on simplistic tools we can create a computationally fast, simple, yet effective approach for biomedical Big Data analysis and knowledge discovery.

Recent sequencing technology breakthroughs have resulted in a dramatic increase in the amount of available sequencing data, enabling major scientific advances in biology and medicine. Nowadays, sequencing transcriptome data of single cells (scRNA-seq) are growing rapidly, posing new challenges in their analysis, mostly due to their high dimensionality. In this paper, we study the problem of visualizing such high-dimensional scRNA-seq data. A new visualization scheme is presented based on a customized distance matrix retrieved by applying independently Nearest Neighbors search through multiple Random Projections. The proposed method is compared against well-known dimensionality reduction and visualization techniques showing its capabilities and performance.

In the past few years, there has been a huge growth in Twitter sentiment analysis having already provided a fair amount of research on sentiment detection of public opinion among Twitter users. Given the fact that Twitter messages are generated constantly with dizzying rates, a huge volume of streaming data is created, thus there is an imperative need for accurate methods for knowledge discovery and mining of this information. Although there exists a plethora of twitter sentiment analysis methods in the recent literature, the researchers have shifted to real-time sentiment identification on twitter streaming data, as expected. A major challenge is to deal with the Big Data challenges arising in Twitter streaming applications concerning both Volume and Velocity. Under this perspective, in this paper, a methodological approach based on open source tools is provided for real-time detection of changes in sentiment that is ultra efficient with respect to both memory consumption and computational cost. This is achieved by iteratively collecting tweets in real time and discarding them immediately after their process. For this purpose, we employ the Lexicon approach for sentiment characterizations, while change detection is achieved through appropriate control charts that do not require historical information. We believe that the proposed methodology provides the trigger for a potential large-scale monitoring of threads in an attempt to discover fake news spread or propaganda efforts in their early stages. Our experimental real-time analysis based on a recent hashtag provides evidence that the proposed approach can detect meaningful sentiment changes across a hashtags lifetime.

DEsubs is a network-based systems biology R package that extracts disease-perturbed subpathways within a pathway network as recorded by RNA-seq experiments. It contains an extensive and customized framework with a broad range of operation modes at all stages of the subpathway analysis, enabling so a case-specific approach. The operation modes include pathway network construction and processing, subpathway extraction, visualization and enrichment analysis with regard to various biological and pharmacological features. Its capabilities render DEsubs a tool-guide for both the modeler and experimentalist for the identification of more robust systems-level drug targets and biomarkers for complex diseases. AVAILABILITY AND IMPLEMENTATION: DEsubs is implemented as an R package following Bioconductor guidelines: http://bioconductor.org/packages/DEsubs/ CONTACT: [email protected] information: Supplementary data are available at Bioinformatics online.

MOTIVATION: In the era of network medicine and the rapid growth of paired time series mRNA/microRNA expression experiments, there is an urgent need for pathway enrichment analysis methods able to capture the time- and condition-specific 'active parts' of the biological circuitry as well as the microRNA impact. Current methods ignore the multiple dynamical 'themes'-in the form of enriched biologically relevant microRNA-mediated subpathways-that determine the functionality of signaling networks across time. RESULTS: To address these challenges, we developed time-vaRying enriCHment integrOmics Subpathway aNalysis tOol (CHRONOS) by integrating time series mRNA/microRNA expression data with KEGG pathway maps and microRNA-target interactions. Specifically, microRNA-mediated subpathway topologies are extracted and evaluated based on the temporal transition and the fold change activity of the linked genes/microRNAs. Further, we provide measures that capture the structural and functional features of subpathways in relation to the complete organism pathway atlas. Our application to synthetic and real data shows that CHRONOS outperforms current subpathway-based methods into unraveling the inherent dynamic properties of pathways. AVAILABILITY AND IMPLEMENTATION: CHRONOS is freely available at http://biosignal.med.upatras.gr/chronos/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.