A5089612533- Nanoparticles: synthesis and applications
- Machine Learning in Materials Science
- Computational Drug Discovery Methods
- Microplastics and Plastic Pollution
- Protein Structure and Dynamics
- Risk and Safety Analysis
- Advanced Chemical Sensor Technologies
- Chemistry and Chemical Engineering
- Gene expression and cancer classification
- IoT and Edge/Fog Computing
- Molecular Biology Techniques and Applications
- Healthcare Technology and Patient Monitoring
- Advanced Data Processing Techniques
- Business Process Modeling and Analysis
- Cell Image Analysis Techniques
- Ecosystem dynamics and resilience
- Scientific Computing and Data Management
- Heavy metals in environment
- Bioinformatics and Genomic Networks
- Machine Learning in Bioinformatics
- Software Reliability and Analysis Research
- Service-Oriented Architecture and Web Services
- Statistical and Computational Modeling
- Environmental Toxicology and Ecotoxicology
- Graphene and Nanomaterials Applications
National Technical University of Athens
2020-2025
The extensive conformational dynamics of partially disordered proteins hinders the efficiency traditional in-silico structure-based drug discovery approaches due to challenge screening large chemical spaces compounds, albeit with an excessive number transient binding sites, quickly making this problem intractable. In study, using monomer AR-V7 transcription factor splicing variant related prostate cancer as a test case, we present deep ensemble docking pipeline that accelerates small...
Abstract This study presents the results of applying deep learning methodologies within ecotoxicology field, with objective training predictive models that can support hazard assessment and eventually design safer engineered nanomaterials (ENMs). A workflow two different architectures on microscopic images Daphnia magna is proposed automatically detect possible malformations, such as effects length tail, overall size, uncommon lipid concentrations deposit shapes, which are due to direct or...
Abstract Partially disordered proteins can contain both stable and unstable secondary structure segments are involved in various (mis)functions the cell. The extensive conformational dynamics of partially scaling with extent disorder length protein hampers efficiency traditional experimental in-silico structure-based drug discovery approaches. Therefore new efficient paradigms taking into account ensembles need to emerge. In this study, using as a test case AR-V7 transcription factor...
The EPA nanoQSAR model predicts the impacts of in vitro cell viability following exposure to certain nanomaterials.
Microarray experiments, a mainstay in gene expression analysis for nearly two decades, pose challenges due to their complexity. To address this, we introduce DExplore, user-friendly web application enabling researchers detect differentially expressed genes using data from NCBI’s GEO. Developed with R, Shiny, and Bioconductor, DExplore integrates WebGestalt functional enrichment analysis. It also provides visualization plots enhanced result interpretation. With Docker image local execution,...
Abstract In recent years, deep neural networks, especially those exhibiting synergistic properties, have been at the cutting edge of image processing, producing very good results. So far, they able to successfully address issues classification and recognition objects depicted on images. this paper, a novel idea is presented, where images chemical structures are used as input information in learning network architectures aiming generation Quantitative Structure Activity Relationship (QSAR)...