A5061175529- MicroRNA in disease regulation
- Single-cell and spatial transcriptomics
- RNA modifications and cancer
- Cancer-related molecular mechanisms research
- Gene expression and cancer classification
- Advanced biosensing and bioanalysis techniques
- COVID-19 diagnosis using AI
- AI in cancer detection
- Radiomics and Machine Learning in Medical Imaging
- Bioinformatics and Genomic Networks
- Protein Degradation and Inhibitors
- RNA Interference and Gene Delivery
- Multiple Myeloma Research and Treatments
- Peptidase Inhibition and Analysis
- Cancer Genomics and Diagnostics
- Neuroinflammation and Neurodegeneration Mechanisms
- Acute Myeloid Leukemia Research
- Gene Regulatory Network Analysis
- RNA and protein synthesis mechanisms
- Genetic Syndromes and Imprinting
- T-cell and B-cell Immunology
- Fish biology, ecology, and behavior
- Cell Image Analysis Techniques
- RNA regulation and disease
- Histone Deacetylase Inhibitors Research
Cyprus Institute of Neurology and Genetics
2023-2025
Thomas Jefferson University
2019-2022
Foundation for Research and Technology Hellas
2010-2021
University of Crete
2011-2019
FORTH Institute of Molecular Biology and Biotechnology
2012-2014
University Hospital of Heraklion
2013
BackgroundMultiple Sclerosis (MS) is a chronic inflammatory disease and leading cause of progressive neurological disability among young adults. DNA methylation, which intersects genes environment to control cellular functions on molecular level, may provide insights into MS pathogenesis.MethodsWe measured methylation in CD4+ T cells (n = 31), CD8+ 28), CD14+ monocytes 35) CD19+ B 27) from relapsing-remitting (RRMS), secondary (SPMS) patients healthy controls (HC) using Infinium...
Technologies for profiling samples using different omics platforms have been at the forefront since human genome project. Large-scale multi-omics data hold promise of deciphering regulatory layers. Yet, while there is a myriad bioinformatics tools, each analysis appears to start from scratch with an arbitrary decision over which tools use and how combine them. Therefore, it unmet need conceptualize integrate such implement validate pipelines in cases. We designed conceptual framework...
Background: The accurate staging of multiple myeloma (MM) is essential for optimizing treatment strategies, while predicting the progression asymptomatic patients, also referred to as monoclonal gammopathy undetermined significance (MGUS), symptomatic MM remains a significant challenge due limited data. This study aimed develop machine learning models enhance accuracy and stratify patients by their risk progression. Methods: We utilized gene expression microarray datasets models, combined...
MicroRNA (miRNA) precursor arms give rise to multiple isoforms simultaneously called 'isomiRs.' IsomiRs from the same arm typically differ by a few nucleotides at either their 5' or 3' termini both. In humans, identities and abundances of isomiRs depend on person's sex genetic ancestry as well tissue type, state disease type/subtype. Moreover, nearly half time most abundant isomiR differs miRNA sequence found in public databases. Accurate mining deep sequencing data is thus important.We...
We address the problem of predicting position a miRNA duplex on microRNA hairpin via development and application novel SVM-based methodology. Our method combines unique representation an unbiased optimization protocol to learn from mirBase19.0 accurate predictive model, termed MiRduplexSVM. This is first model that provides precise information about all four ends duplex. show (a) our outperforms state-of-the-art tools, namely MaturePred, MiRPara, MatureBayes, MiRdup as well Simple Geometric...
Single-cell RNA-sequencing (scRNAseq) technologies are rapidly evolving. Although very informative, in standard scRNAseq experiments, the spatial organization of cells tissue origin is lost. Conversely, RNA-seq designed to maintain cell localization have limited throughput and gene coverage. Mapping genes with information increases coverage while providing location. However, methods perform such mapping not yet been benchmarked. To fill this gap, we organized DREAM Single-Cell...
Computational methods for miRNA target prediction vary in the algorithm used; and while one can state opinions about strengths or weaknesses of each particular algorithm, fact matter is that they fall substantially short capturing full detail physical, temporal spatial requirements miRNA::target-mRNA interactions. Here, we introduce a novel tool called Targetprofiler utilizes probabilistic learning form hidden Markov model trained on experimentally verified targets. Using large scale protein...
Abstract Multiple Sclerosis (MS) is an autoimmune disease of the central nervous system with prominent neurodegenerative components. The triggering and progression MS associated transcriptional epigenetic alterations in several tissues, including peripheral blood. combined influence changes has not been assessed same individuals. Here we generated paired transcriptomic (RNA-seq) DNA methylation (Illumina 450 K array) profiles CD4+ CD8+ T cells (CD4, CD8), using clinically accessible blood...
The advance of omics technologies has made possible to measure several data modalities on a system interest. In this work, we illustrate how the Non-Parametric Combination methodology, namely NPC, can be used for simultaneously assessing association different molecular quantities with an outcome We argue that NPC methods have potential applications in integrating heterogeneous technologies, as example identifying genes whose methylation and transcriptional levels are jointly deregulated, or...
Summary Introduction The association between the risk of acute lymphoblastic leukemia ( ALL ) in children and enzymes involved folate metabolism has been under investigation lately. reduced carrier gene RFC encodes carrier, a protein that transports into cell both methotrexate, commonly used chemotherapeutic drug, proved polymorphic at position 80 (G→A). role this polymorphism childhood its interaction with other metabolic pathway, including MTHFR , examined different populations diverse...
The "replication crisis" is a methodological problem in which many scientific research findings have been difficult or impossible to replicate. Because the reproducibility of empirical results an essential aspect method, such failures endanger credibility theories based on them and possibly significant portions knowledge. An instance replication crisis, analytic replication, pertains reproducing published through computational reanalysis authors' original data. However, direct replications...
Abstract Single-cell RNA-seq technologies are rapidly evolving but while very informative, in standard scRNAseq experiments the spatial organization of cells tissue origin is lost. Conversely, designed to keep localization have limited throughput and gene coverage. Mapping genes with information increases coverage providing location. However, methods perform such mapping not yet been benchmarked. To bridge gap, we organized DREAM Single-Cell Transcriptomics challenge focused on...
The development of single-cell sequencing technologies has allowed researchers to gain important new knowledge about the expression profile genes in thousands individual cells a model organism or tissue. A common disadvantage this technology is loss three-dimensional (3-D) structure cells. Consequently, Dialogue on Reverse Engineering Assessment and Methods (DREAM) organized Single-Cell Transcriptomics Challenge, which we participated, with aim address following two problems: (a) identify...
The COVID-19 pandemic has exemplified the importance of interoperable and equitable data sharing for global surveillance to support research. While many challenges could be overcome, at least in some countries, hurdles within organizational, scientific, technical cultural realms still remain tackled prepared future threats. We propose (i) continue supporting efforts that have proven efficient trustworthy toward addressing pathogen molecular sharing; (ii) establish a distributed network...
Abstract Background and Objective The standard of care in Acute Myeloid Leukemia patients has remained essentially unchanged for nearly 40 years. Due to the complicated mutational patterns within between individual a lack targeted agents most events, implementing individualized treatment AML proven difficult. We reanalysed BeatAML dataset employing Machine Learning algorithms . project entails extensively characterized at molecular clinical levels linked drug sensitivity outputs. Our...
Prioritization or ranking of different cell types in a single-cell RNA sequencing (scRNA-seq) framework can be performed variety ways, some these include: i) obtaining an indication the proportion between conditions under study, ii) counting number differentially expressed genes (DEGs) and experiment or, iii) prioritizing based on prior knowledge about study (i.e., specific disease). These methods have drawbacks limitations thus novel for improving are required. Here we present methodology...
Abstract Background/aim Multiple Myeloma is the second most common blood cancer, characterised by accumulation of malignant plasma cells and production large amounts a monoclonal immunoglobulin protein, in bone marrow. The identification progression/behaviour molecular markers across stages remains scientific challenge. This work aims to provide holistic approach understanding disease progression, providing specific methodologies candidate biomarkers, able characterise distinguish state...
Abstract In this study, we developed and evaluated Machine Learning (ML) models aimed at predicting the stage of multiple myeloma (MM) progression monoclonal gammopathy undetermined significance (MGUS) to MM. Accurate staging MM is critical for determining appropriate treatment strategies, our models, employing algorithms such as ElasticNet, Random Forest, Boosting, Support Vector Machines, demonstrated high efficacy in capturing biological differences across disease stages. Among these,...
Abstract Motivation We participated in the DREAM Single Cell Transcriptomics Challenge. The challenge’s focus was two-fold; a) to identify top 60, 40 and 20 genes that contain most spatial information, b) reconstruct 3-D arrangement of D. melanogaster embryo using information from those genes. Results developed two independent approaches, leveraging machine learning models Lasso Deep Neural Networks, we successfully apply high-dimensional single-cell sequencing data. Our methods allowed us...