Publications
Book Chapters Journals Conferences and Announcements Other Theses
Book Chapters
- P. Nousi, S.-C. Fragkouli, N. Stergioulas, and A. Tefas, “Deep Learning Methods for Accelerating Gravitational Wave Surrogate Modeling,” in Gravitational Wave Science with Machine Learning, E. Cuoco, Ed. Singapore: Springer Nature Singapore, 2025, pp. 275–288.
We explore the application of deep learning techniques to accelerate gravitational wave surrogate modeling. We focus on two recent approaches, using artificial neural networks (ANNs) with residual error modeling and autoencoder-driven spiral representation learning. For the ANN method, we demonstrate that adding a second network to learn residual errors significantly improves surrogate model accuracy. The autoencoder approach reveals an inherent spiral structure in the latent space representation of empirical interpolation coefficients. We take advantage of this insight to develop a neural spiral module that can be integrated into network architectures to accelerate training and improve performance. Comprehensive evaluations show that these methods achieve state-of-the-art accuracy while enabling faster waveform generation. The techniques presented have the potential to substantially accelerate gravitational wave data analysis as detector sensitivity improves and event rates increase.
Journals
- S.-C. Fragkouli, N. Pechlivanis, A. Anastasiadou, G. Karakatsoulis, A. Orfanou, P. Kollia, A. Agathangelidis, and F. Psomopoulos, “Synth4bench: generating synthetic data for benchmarking tumor-only somatic variant calling algorithms,” Frontiers in Bioinformatics, vol. Volume 6 - 2026, 2026, doi: 10.3389/fbinf.2026.1858375.
BackgroundSomatic variant calling is a key activity towards identifying genomic alterations; yet, the evaluation of the respective tools remains challenging due to the scarcity of high quality ground truth datasets. To overcome this limitation, we developed synth4bench, a synthetic data generation pipeline, which utilizes the NEAT simulator, for robust benchmarking. Using a systematic process to create distinct synthetic datasets, we thoroughly evaluated five variant callers (Mutect2, FreeBayes, VarDict, VarScan2 and LoFreq). We compared tool outputs against our synthetic ground truth across key sequencing aspects (such as depth and read length) to assess their capacities and shed light on their underlying algorithmic principles.ResultsSynth4bench is an approach for evaluating tumor-only somatic variant callers that relies on a systematic definition of fully controlled ground-truth datasets. Our analysis revealed significant inconsistencies among the tool outputs and a strong dependence of caller performance on sequencing parameters. Indels remain the hardest-to-call variant type, driven by errors at low allele frequencies. Algorithmic choice is also critical; the most robust callers displayed the highest Precision in allele frequency estimation, while the most sensitive caller was best for maximizing true positive recovery. Conversely, the least suitable caller exhibited systematic errors along with the poorest overall performance.ConclusionThese findings indicate that there is not a one-size-fits-all approach; sequencing optimization together with caller selection are necessary to maximize sensitivity and reliability. Furthermore, the pronounced inconsistencies suggest that current algorithms are not yet able to capture all mutational mechanisms adequately, with the modeling of the underlying processes remaining an open challenge.
- A. Pseftogas, J. Bordini, G. Gavriilidis, M. Frenquelli, A. Campanella, A. Rovida, G. Morello, M. Gerousi, E. Theodosiou, S.-C. Fragkouli, V. Vasileiou, T. Sklaviadis, D. Dafou, G. Mosialos, C. Tripodo, F. Psomopoulos, T. H. Winkler, K. Stamatopoulos, P. Ghia, and K. Xanthopoulos, “The deubiquitinase activity of CYLD is required for B cell differentiation,” Cell Death & Disease, 2026, doi: 10.1038/s41419-026-08555-x.
CYLD is a functional deubiquitinase, involved in the regulation of significant cellular functions, including survival and apoptosis. To elucidate the role of CYLD in B cell differentiation, we generated transgenic animals with targeted deletion of the catalytically active form of the protein in B cells, starting from early differentiation stages. Our results indicate that catalytic inactivation of CYLD leads to a severe reduction of mature B cells, associated with blockade of differentiation at the Pro B cell stage, altered distribution of B cell populations in the spleen and bone marrow, culminating in impaired immune responses to model antigens. Single cell RNA sequencing of bone marrow B cells confirmed the severe perturbation of lymphopoiesis. Mechanistically, we found impaired expression of the IL-7 receptor alpha chain (IL-7Ra) and its upstream transcriptional activator FOXO1, leading to defective IL-7 signaling that is vital for early B cell development. However, the substrate(s) deubiquitinated by CYLD that regulates the FOXO1-IL-7R pathway remains unclear. Overall, our data imply a crucial role for the deubiquitinase activity of CYLD in B cell lymphopoiesis.
- G. Farrell, E. Adamidi, R. Andrade Buono, M. Anton, O. A. Attafi, S. Capella-Gutierrez, E. Capriotti, L. J. Castro, D. Cirillo, L. Crossman, C. Dessimoz, A. Dimopoulos, R. Fernández-Díaz, S.-C. Fragkouli, C. Goble, W. Gu, J. M. Hancock, A. Khanteymoori, T. Lenaerts, F. G. Liberante, P. Maccallum, A. M. Monzon, M. Palmblad, L. Poveda, O. Radulescu, D. C. Shields, S. Sufi, T. Vergoulis, F. Psomopoulos, and S. C. E. Tosatto, “Open and sustainable AI: challenges, opportunities and the road ahead in the life sciences,” Nature Methods, 2026, doi: 10.1038/s41592-026-03037-6.
Artificial intelligence (AI) has seen transformative breakthroughs in the life sciences, expanding possibilities to interpret biological information at an unprecedented capacity. To maximize return on growing investments and accelerate progress, it is urgent to address long-standing research challenges arising from the rapid adoption of AI methods. We review the erosion of trust in AI outputs driven by poor reusability and reproducibility, and highlight their impact on environmental sustainability. Furthermore, we discuss the fragmented components of the AI ecosystem and lack of guiding pathways to support open and sustainable AI model development. In response, this Perspective introduces practical open and sustainable AI recommendations mapped to over 300 ecosystem components and provides guiding implementation pathways. Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and reproducible AI. Built upon community consensus and aligned to existing efforts, these outputs will aid future policy development and structured pathways for guiding AI implementation.
- S.-C. Fragkouli, S. Iqbal, L. Crossman, B. Gravel, N. Masued, M. Onders, D. Haseja, A. Stikkelman, A. Valencia, T. Lenaerts, F. Psomopoulos, P. Ó Broin, N. Queralt-Rosinach, and D. Cirillo, “An ELIXIR scoping review on domain-specific evaluation metrics for synthetic data in life sciences,” NAR Genomics and Bioinformatics, vol. 8, no. 1, p. lqag012, Feb. 2026, doi: 10.1093/nargab/lqag012.
Synthetic data (SD) has become an increasingly important asset in the life sciences, helping address data scarcity, privacy concerns, and barriers to data access. Creating artificial datasets that mirror the characteristics of real data allows researchers to develop and validate computational methods in controlled environments. Despite its promise, the adoption of SD in life sciences hinges on rigorous evaluation metrics designed to assess their fidelity and reliability. To explore the current landscape of SD evaluation metrics in distinct life sciences domains, the ELIXIR Machine Learning Focus Group performed a systematic review of the scientific literature following the PRISMA guidelines. Six critical domains were examined to identify current practices for assessing SD. Findings reveal that, while generation methods are rapidly evolving, systematic evaluation is often overlooked, limiting researchers’ ability to compare, validate, and trust synthetic datasets across different domains. This systematic review underscores the urgent need for robust, standardized evaluation approaches that not only bolster confidence in SD but also guide its effective and responsible implementation. By laying the groundwork for establishing domain-specific yet interoperable standards, this scoping review paves the way for future initiatives aimed at enhancing the role of SD in scientific discovery, clinical practice and beyond.
- O. A. Attafi, D. Clementel, K. Kyritsis, E. Capriotti, G. Farrell, S.-C. Fragkouli, L. J. Castro, A. Hatos, T. Lenaerts, S. Mazurenko, S. Mozaffari, F. Pradelli, P. Ruch, C. Savojardo, P. Turina, F. Zambelli, D. Piovesan, A. M. Monzon, F. Psomopoulos, and S. C. E. Tosatto, “DOME Registry: implementing community-wide recommendations for reporting supervised machine learning in biology,” GigaScience, vol. 13, p. giae094, Dec. 2024, doi: 10.1093/gigascience/giae094.
Supervised machine learning (ML) is used extensively in biology and deserves closer scrutiny. The Data Optimization Model Evaluation (DOME) recommendations aim to enhance the validation and reproducibility of ML research by establishing standards for key aspects such as data handling and processing, optimization, evaluation, and model interpretability. The recommendations help to ensure that key details are reported transparently by providing a structured set of questions. Here, we introduce the DOME registry (URL: registry.dome-ml.org), a database that allows scientists to manage and access comprehensive DOME-related information on published ML studies. The registry uses external resources like ORCID, APICURON, and the Data Stewardship Wizard to streamline the annotation process and ensure comprehensive documentation. By assigning unique identifiers and DOME scores to publications, the registry fosters a standardized evaluation of ML methods. Future plans include continuing to grow the registry through community curation, improving the DOME score definition and encouraging publishers to adopt DOME standards, and promoting transparency and reproducibility of ML in the life sciences.
- S.-C. Fragkouli, D. Solanki, L. Castro, F. Psomopoulos, N. Queralt-Rosinach, D. Cirillo, and L. Crossman, “Synthetic data: how could it be used in infectious disease research?,” Future Microbiology, vol. 0, no. 0, pp. 1–6, 2024, doi: 10.1080/17460913.2024.2400853.
- S.-C. Fragkouli, P. Nousi, N. Passalis, P. Iosif, N. Stergioulas, and A. Tefas, “Deep residual error and bag-of-tricks learning for gravitational wave surrogate modeling,” Applied Soft Computing, p. 110746, 2023, doi: https://doi.org/10.1016/j.asoc.2023.110746.
Deep learning methods have been employed in gravitational-wave astronomy to accelerate the construction of surrogate waveforms for the inspiral of spin-aligned black hole binaries, among other applications. We face the challenge of modeling the residual error of an artificial neural network that models the coefficients of the surrogate waveform expansion (especially those of the phase of the waveform) which we demonstrate has sufficient structure to be learnable by a second network. Adding this second network, we were able to reduce the maximum mismatch for waveforms in a validation set by 13.4 times. We also explored several other ideas for improving the accuracy of the surrogate model, such as the exploitation of similarities between waveforms, the augmentation of the training set, the dissection of the input space, using dedicated networks per output coefficient and output augmentation. In several cases, small improvements can be observed, but the most significant improvement still comes from the addition of a second network that models the residual error. Since the residual error for more general surrogate waveform models (when e.g., eccentricity is included) may also have a specific structure, one can expect our method to be applicable to cases where the gain in accuracy could lead to significant gains in computational time.
- L. Zaragoza-Infante, V. Junet, N. Pechlivanis, S.-C. Fragkouli, S. Amprachamian, T. Koletsa, A. Chatzidimitriou, M. Papaioannou, K. Stamatopoulos, A. Agathangelidis, and F. Psomopoulos, “IgIDivA: immunoglobulin intraclonal diversification analysis,” Briefings in Bioinformatics, Aug. 2022, doi: 10.1093/bib/bbac349.
Intraclonal diversification (ID) within the immunoglobulin (IG) genes expressed by B cell clones arises due to ongoing somatic hypermutation (SHM) in a context of continuous interactions with antigen(s). Defining the nature and order of appearance of SHMs in the IG genes can assist in improved understanding of the ID process, shedding light into the ontogeny and evolution of B cell clones in health and disease. Such endeavor is empowered thanks to the introduction of high-throughput sequencing in the study of IG gene repertoires. However, few existing tools allow the identification, quantification and characterization of SHMs related to ID, all of which have limitations in their analysis, highlighting the need for developing a purpose-built tool for the comprehensive analysis of the ID process. In this work, we present the immunoglobulin intraclonal diversification analysis (IgIDivA) tool, a novel methodology for the in-depth qualitative and quantitative analysis of the ID process from high-throughput sequencing data. IgIDivA identifies and characterizes SHMs that occur within the variable domain of the rearranged IG genes and studies in detail the connections between identified SHMs, establishing mutational pathways. Moreover, it combines established and new graph-based metrics for the objective determination of ID level, combined with statistical analysis for the comparison of ID level features for different groups of samples. Of importance, IgIDivA also provides detailed visualizations of ID through the generation of purpose-built graph networks. Beyond the method design, IgIDivA has been also implemented as an R Shiny web application. IgIDivA is freely available at https://bio.tools/igidiva
- P. Nousi, S.-C. Fragkouli, N. Passalis, P. Iosif, T. Apostolatos, G. Pappas, N. Stergioulas, and A. Tefas, “Autoencoder-driven spiral representation learning for gravitational wave surrogate modelling,” Neurocomputing, vol. 491, pp. 67–77, 2022, doi: 10.1016/j.neucom.2022.03.052.
Recently, artificial neural networks have been gaining momentum in the field of gravitational wave astronomy, for example in surrogate modelling of computationally expensive waveform models for binary black hole inspiral and merger. Surrogate modelling yields fast and accurate approximations of gravitational waves and neural networks have been used in the final step of interpolating the coefficients of the surrogate model for arbitrary waveforms outside the training sample. We investigate the existence of underlying structures in the empirical interpolation coefficients using autoencoders. We demonstrate that when the coefficient space is compressed to only two dimensions, a spiral structure appears, wherein the spiral angle is linearly related to the mass ratio. Based on this finding, we design a spiral module with learnable parameters, that is used as the first layer in a neural network, which learns to map the input space to the coefficients. The spiral module is evaluated on multiple neural network architectures and consistently achieves better speed-accuracy trade-off than baseline models. A thorough experimental study is conducted and the final result is a surrogate model which can evaluate millions of input parameters in a single forward pass in under 1 ms on a desktop GPU, while the mismatch between the corresponding generated waveforms and the ground-truth waveforms is better than the compared baseline methods. We anticipate the existence of analogous underlying structures and corresponding computational gains also in the case of spinning black hole binaries.
Conferences and Announcements
- S.-C. Fragkouli, S. Iqbal, L. Crossman, B. Gravel, N. Masued, M. Onders, D. Haseja, A. Stikkelman, A. Valencia, T. Lenaerts, F. Psomopoulos, P. Ó Broin, N. Queralt-Rosinach, and D. Cirillo, “Synthetic Data Evaluation Metrics in Life Sciences: An ELIXIR Scoping Review,” 25rd European Conference on Computational Biology (ECCB2026), Aug. 2026.
- S.-C. Fragkouli, N. Pechlivanis, A. Anastasiadou, G. Karakatsoulis, A. Orfanou, P. Kollia, A. Agathangelidis, and F. Psomopoulos, “Benchmarking Somatic Variant CallersA Tale Unfolding In The Synthetic Genomics Feature Space,” 23rd European Conference on Computational Biology (ECCB2024), Nov. 2024, doi: 10.5281/zenodo.14186510.
- S.-C. Fragkouli, N. Pechlivanis, A. Orfanou, A. Anastasiadou, A. Agathangelidis, and F. Psomopoulos, “Synth4bench: a framework for generating synthetic genomics data for the evaluation of somatic variant calling algorithms, 17th Conference of Hellenic Society for Computational Biology and Bioinformatics (HSCBB),” Oct. 2023, doi: 10.5281/zenodo.8432060.
- S.-C. Fragkouli, “Synthetic genomics data generation and evaluation for the use case of benchmarking somatic variant calling algorithms, 31st Conference in Intelligent Systems For Molecular Biology and the 22nd European Conference On Computational Biology (ISΜB-ECCB23) ,” Jul. 2023, doi: 10.7490/f1000research.1119576.1.
- S.-C. Fragkouli, N. Pechlivanis, A. Agathangelidis, and F. Psomopoulos, “Synthetic Genomics Data Generation and Evaluation for the Use Case of Benchmarking Somatic Variant Calling Algorithms, 31st Conference in Intelligent Systems For Molecular Biology and the 22nd European Conference On Computational Biology (ISMB-ECCB23),” Jul. 2023, doi: 10.7490/f1000research.1119575.1.
- G. Gavriilidis, S.-C. Fragkouli, E. Theodosiou, V. Vasileiou, S. Keisaris, and F. Psomopoulos, “SCell-wise fluxomics of Chronic Lymphocytic Leukemia single-cell data reveal novel metabolic adaptations to Ibrutinib therapy, 31st Conference in Intelligent Systems For Molecular Biology and the 22nd European Conference On Computational Biology (ISΜB-ECCB23) ,” Jul. 2023, doi: 10.13140/RG.2.2.14185.26720.
- S.-C. Fragkouli, A. Agathangelidis, and F. Psomopoulos, “Shedding Light on Somatic Variant Calling, 16th Conference of the Hellenic Society for Computational Biology and Bioinformatics HSCBB22,” Oct. 2022, doi: 10.13140/RG.2.2.12701.18402.
Other
- G. Farrell, O. A. Attafi, S.-C. Fragkouli, I. Heredia, S. Fernández Tobias, M. Harrison, H. Hermjakob, M. Jeffryes, M. Mehdiabadi, M. Óbregon Ruiz, M. Pearce, N. Pechlivanis, F. Quaglia, López Garcı́a Alvaro, F. Psomopoulos, and S. C. E. Tosatto, “DOME Copilot: A resource to automate transparent reporting of artificial intelligence methods,” bioRxiv. Cold Spring Harbor Laboratory, 2026, doi: 10.64898/2026.04.16.718888.
Artificial intelligence (AI) methods are transforming life science research and witnessing unprecedented adoption across literature. While AI is driving impactful discoveries, this rapid growth in application has inundated researchers with poorly described models and datasets which lack transparency, impeding reusability and eroding trust. The DOME Recommendations aimed to address this issue and established structured reporting guidelines to standardize AI methodology descriptions. However, the need to manually create these comprehensive disclosures imposed a major bottleneck, requiring substantial effort from authors to comply. To bridge this gap, we present DOME Copilot, an open-source, easily deployable system that automates the generation of transparent AI methodology reports supplementing publications. Evaluated against a benchmark of human-created annotations, DOME Copilot was determined to match or exceed manual annotation quality across the majority of reporting fields while reducing the creation time from hours to minutes. Ultimately, the system provides a novel and scalable solution to restore confidence in complex life science AI method publications.Competing Interest StatementThe authors have declared no competing interest.
- G. Farrell, O. A. Attafi, S.-C. Fragkouli, I. Heredia, S. Fernández Tobias, M. Harrison, H. Hermjakob, M. Jeffryes, M. Óbregon Ruiz, M. Pearce, N. Pechlivanis, A. López García, F. Psomopoulos, and S. C. E. Tosatto, “DOME Copilot: Making transparency and reproducibility for artificial intelligence methods simple,” bioRxiv. 2026, doi: 10.64898/2026.04.16.718888.
Unprecedented breakthroughs are being made in life science research through the application of artificial intelligence (AI). However, adherence to method reporting guidelines is necessary to support their reusability and reproducibility. The DOME Copilot solution extracts structured reports of AI methods using a large language model to help interpret manuscripts. It is a fast and efficient resource capable of scaling to annotate the corpus of global AI literature, unlocking value and trust in published methods.Competing Interest StatementThe authors have declared no competing interest.
- S.-C. Fragkouli, “Φυσική, Φυσικοί και… το CERN!” Zenodo, Mar. 2026, doi: 10.5281/zenodo.19354765.
- S.-C. Fragkouli and N. Anastasiadou, “sfragkoul/synth4bench: synth4bench Release.” Zenodo, Mar. 2026, doi: 10.5281/zenodo.19049171.
- S.-C. Fragkouli, N. Pechlivanis, A. Anastasiadou, G. Karakatsoulis, A. Orfanou, P. Kollia, A. Agathangelidis, and F. E. Psomopoulos, “Synth4bench: Synthetic Data Generation for Benchmarking Tumor-Only Somatic Variant Calling Algorithms.” 2025, doi: 10.1101/2024.03.07.582313.
- F. E. Psomopoulos and S.-C. Fragkouli, “Regulations/standards for AI using DOME (Galaxy Training Materials).” 2025, [Online]. Available at: https://training.galaxyproject.org/training-material/topics/statistics/tutorials/dome/tutorial.html.
- S.-C. Fragkouli, A. Agathangelidis, and F. E. Psomopoulos, “25 Synthetic TP53 Genomic Datasets for Benchmarking and Method Development.” Oct. 2025, doi: 10.5281/zenodo.16524193.
- S.-C. Fragkouli, S. Iqbal, L. Crossman, B. Gravel, N. Masued, M. Onders, D. Haseja, A. Stikkelman, A. Valencia, T. Lenaerts, F. Psomopoulos, P. Ó. Broin, N. Queralt-Rosinach, and D. Cirillo, “An ELIXIR scoping review on domain-specific evaluation metrics for synthetic data in life sciences.” 2025, [Online]. Available at: https://arxiv.org/abs/2506.14508.
- SYNTHIA Consortium, “SYNTHIA State of the Art Suvey on SDG D6.1.” Zenodo, Feb. 2025, doi: 10.5281/zenodo.15525209.
- G. Farrell, E. Adamidi, R. A. Buono, M. Anton, O. A. Attafi, S. C. Gutierrez, E. Capriotti, L. J. Castro, D. Cirillo, L. Crossman, C. Dessimoz, A. Dimopoulos, R. Fernandez-Diaz, S.-C. Fragkouli, C. Goble, W. Gu, J. M. Hancock, A. Khanteymoori, T. Lenaerts, F. G. Liberante, P. Maccallum, A. M. Monzon, M. Palmblad, L. Poveda, O. Radulescu, D. C. Shields, S. Sufi, T. Vergoulis, F. Psomopoulos, and S. C. E. Tosatto, “Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences.” 2025, [Online]. Available at: https://arxiv.org/abs/2505.16619.
- S.-C. Fragkouli and F. E. Psomopoulos, “WP11 –NGS INCLUDING LIQUID BIOPSY,” CAN.Heal consortium meeting. Oct. 2024, doi: 10.5281/zenodo.13959394.
- O. A. Attafi, D. Clementel, K. Kyritsis, E. Capriotti, G. Farrell, S.-C. Fragkouli, L. J. Castro, A. Hatos, T. Lenaerts, S. Mazurenko, S. Mozaffari, F. Pradelli, P. Ruch, C. Savojardo, P. Turina, F. Zambelli, D. Piovesan, A. M. Monzon, F. Psomopoulos, and S. C. E. Tosatto, “DOME Registry: Implementing community-wide recommendations for reporting supervised machine learning in biology.” 2024, [Online]. Available at: https://arxiv.org/abs/2408.07721.
- F. Psomopoulos, E. Capriotti, N. Rosinach, D. Cirillo, L. Castro, S. Tosatto, and the ELIXIR ML Focus Group members, “The impact of the ELIXIR community in Machine Learning.” ELIXIR All Hands Meeting, Jun. 2024, doi: 10.7490/f1000research.1119794.1.
- S.-C. Fragkouli, N. Pechlivanis, A. Anastasiadou, G. Karakatsoulis, A. Orfanou, P. Kollia, A. Agathangelidis, and F. E. Psomopoulos, “Exploring Somatic Variant Callers’ Behavior: A Synthetic Genomics Feature Space Approach.” ELIXIR All Hands Meeting, Jun. 2024, doi: 10.7490/f1000research.1119793.1.
- S.-C. Fragkouli, D. Solanki, L. J. Castro, F. E. Psomopoulos, N. Queralt-Rosinach, D. Cirillo, and L. C. Crossman, “Synthetic data: How could it be used for infectious disease research?” 2024, [Online]. Available at: https://arxiv.org/abs/2407.06211.
- S.-C. Fragkouli and F. E. Psomopoulos, “WP11 –NGS INCLUDING LIQUID BIOPSY,” CAN.Heal consortium meeting. Apr. 2024, doi: 10.5281/zenodo.13843594.
- S.-C. Fragkouli, A. Agathangelidis, and F. E. Psomopoulos, “10 Synthetic Genomics Datasets.” Feb. 2024, doi: 10.5281/zenodo.10683211.
- F. Adriano, E. Parkinson, D. Bianchini, F. Psomopoulos, M. Varadi, M. Andrabi, S.-C. Fragkouli, and U. Vadadokhau, “RDMkit, Your Domain, Machine Learning.” 2024, [Online]. Available at: https://rdmkit.elixir-europe.org/machine_learning.
- S.-C. Fragkouli, A. Agathangelidis, and F. E. Psomopoulos, “TP53 synthetic genomics data for benchmarking variant callers.” Jun. 2023, doi: 10.5281/zenodo.8095898.
- S.-C. Fragkouli, “DOME ANNOTATION: THE CROWDSOURCING EFFORT,” ELIXIR DOME Strategic Implementation Study Kickoff meeting. Apr. 2023, doi: 10.5281/zenodo.10688491.
- N. Pechlivanis, M. Tsagiopoulou, M. C. Maniou, A. Togkousidis, E. Mouchtaropoulou, T. Chassalevris, S. Chaintoutis, C. Dovas, M. Petala, M. Kostoglou, T. Karapantsios, S. Laidou, E. Vlachonikola, A. Orfanou, S.-C. Fragkouli, S. Keisaris, A. Chatzidimitriou, A. Papadopoulos, N. Papaioannou, A. Argiriou, and F. E. Psomopoulos, “Detection of SARS-CoV-2 lineages in wastewater samples using next-generation sequencing.” 2022, doi: https://doi.org/10.18129/B9.bioc.lineagespot.
- S.-C. Fragkouli, P. Nousi, N. Passalis, P. Iosif, N. Stergioulas, and A. Tefas, “Deep Residual Error and Bag-of-Tricks Learning for Gravitational Wave Surrogate Modeling.” arXiv, 2022, doi: 10.48550/ARXIV.2203.08434.
- S.-C. Fragkouli, “Deep Residual Error and Bag-of-Tricks Learning for Gravitational Wave Surrogate Μodeling,” LIGO-Virgo-Kagra Collaboration Waveform WG meeting. Feb. 2022, doi: 10.5281/zenodo.10688323.
- S.-C. Fragkouli, P. Nousi, N. Passalis, P. Iosif, N. Stergioulas, and A. Tefas, “Deep Residual Error and Bag-of-Tricks Learning for Gravitational Wave Surrogate Modeling,” G2net WG1(Machine Learning for Gravitational Wave Astronomy) meeting. Apr. 2022.
Theses
- S.-C. Fragkouli, “Deep Learning Applications on Gravitational Waves.” Informatics Department, Aristotle University of Thessaloniki, 2021, [Online]. Available at: https://ikee.lib.auth.gr/record/335161.
- S.-C. Fragkouli, “Study of the merging of a neutron star with a black hole and the role of the equation of state.” Physics Department, Aristotle University of Thessaloniki, 2017, [Online]. Available at: http://ikee.lib.auth.gr/record/294627/files/thesis.pdf.