A5032210066- Single-cell and spatial transcriptomics
- Neuroinflammation and Neurodegeneration Mechanisms
- Gene expression and cancer classification
- Bioinformatics and Genomic Networks
- Gene Regulatory Network Analysis
- Machine Learning and ELM
- Domain Adaptation and Few-Shot Learning
- Artificial Intelligence in Healthcare
- Immune cells in cancer
- Cell Image Analysis Techniques
- Anomaly Detection Techniques and Applications
- Privacy-Preserving Technologies in Data
- Privacy, Security, and Data Protection
- CRISPR and Genetic Engineering
- Blockchain Technology Applications and Security
- Extracellular vesicles in disease
- Computational Drug Discovery Methods
- Alzheimer's disease research and treatments
- Machine Learning in Healthcare
- Brain Tumor Detection and Classification
- COVID-19 diagnosis using AI
- Multimodal Machine Learning Applications
- Advanced Graph Neural Networks
- Radiomics and Machine Learning in Medical Imaging
- MicroRNA in disease regulation
Ionian University
2023-2025
Background Tacrolimus, an approved first-line calcineurin inhibitor, is widely prescribed in organ transplantation to prevent allograft rejection. Its narrow therapeutic index requires precise management achieve optimal dosing and minimize adverse drug events (ADEs) while ensuring its efficacy. Among several factors, genetic differences contribute significantly the inter-individual inter-ethnic variability pharmacokinetics (PK) of tacrolimus kidney transplant recipients. As a result,...
Alzheimer’s disease (AD) represents one of the most important healthcare challenges current century, characterized as an expanding, “silent pandemic”. Recent studies suggest that peripheral immune system may participate in AD development; however, molecular components these cells remain poorly understood. Although single-cell RNA sequencing (scRNA-seq) offers a sufficient exploration various biological processes at cellular level, number existing works is limited, and no comprehensive...
As data become increasingly abundant and diverse, their potential to fuel machine learning models is vast. However, traditional centralized approaches, which require aggregating into a single location, face significant challenges. Privacy concerns, stringent protection regulations like GDPR, the high cost of transmission hinder feasibility centralizing sensitive from disparate sources such as hospitals, financial institutions, personal devices. Federated Learning addresses these issues by...
The progressive aging of the global population and high impact neurodegenerative diseases, such as Alzheimer’s disease (AD), underscore urgent need for innovative diagnostic therapeutic strategies. AD, most prevalent disorder among elderly, is expected to affect 75 million people in developing countries by 2030. Despite extensive research, precise etiology AD remains elusive due its heterogeneity complexity. key pathological features including amyloid-beta plaques hyperphosphorylated tau...
The evolution of single-cell technology is ongoing, continually generating massive amounts data that reveal many mysteries surrounding intricate diseases. However, their drawbacks continue to constrain us. Among these, annotating cell types in gene expressions pose a substantial challenge, despite the myriad tools at our disposal. rapid growth data, resources, and has consequently brought about significant alterations this area over years. In study, we spotlight all note-worthy type...
Graph Neural Networks (GNN) are reshaping our understanding of biomedicine and diseases by revealing the deep connections among genes cells. As both algorithmic biomedical technologies have advanced significantly, we're entering a transformative phase personalized medicine. While pioneering tools like Attention (GAT) Convolutional (Graph CNN) advancing graph-based learning, rise single-cell sequencing techniques is insights on cellular diversity function. Numerous studies combined GNNs with...
Alzheimer’s disease (AD) is a complex neurological disorder whose underlying mechanisms remain elusive to this day. Molecular biology methodologies, especially techniques like single-cell RNA sequencing (scRNA-seq), offer unparalleled granularity in deciphering the disease’s cellular intricacies. However, despite potential of scRNA-seq, comprehensive machine-learning analyses are yet be fully harnessed. Emphasizing multi-omics machine-learning-based approaches, which integrate diverse omics...
Cancer remains a pervasive and formidable disease within modern societies, necessitating the utilization of advanced techniques in both diagnosis therapy. Molecular biology has emerged as crucial tool deciphering underlying biological mechanisms that contribute to various types cancer. Notably, single-cell sequencing garnered significant attention state-of-the-art method for profiling gene expression individual cells, unveiling previously concealed phenomena. With abundance datasets...
Alzheimer's Disease (AD) remains a formidable challenge in neurodegenerative research, necessitating innovative approaches to uncover novel therapeutic strategies. This study presents graph-based approach integrate large-scale drug and protein data, aiming identify potential repurposing candidates for AD. Our methodology constructs comprehensive graph incorporating protein-protein interactions drug-protein relations, providing multifaceted view of the intricate relationships within...