Savvas Paragkamian

@article{kokoli_arena3dweb_2023,
title = {Arena3Dweb: interactive 3D visualization of multilayered networks supporting multiple directional information channels, clustering analysis and application integration},
author = {Maria Kokoli and Evangelos Karatzas and Fotis A Baltoumas and Reinhard Schneider and Evangelos Pafilis and Savvas Paragkamian and Nadezhda T Doncheva and Lars Juhl Jensen and Georgios A Pavlopoulos},
url = {https://imbbc.hcmr.gr/wp-content/uploads/2023/06/2023-Kokoli-GenBioinformatics-31.pdf
https://academic.oup.com/nargab/article/doi/10.1093/nargab/lqad053/7185857},
doi = {10.1093/nargab/lqad053},
issn = {2631-9268},
year = {2023},
date = {2023-06-19},
urldate = {2023-06-19},
journal = {NAR Genomics and Bioinformatics},
volume = {5},
number = {2},
pages = {lqad053},
abstract = {Abstract
Arena3Dweb is an interactive web tool that visualizes multi-layered networks in 3D space. In this update, Arena3Dweb supports directed networks as well as up to nine different types of connections between pairs of nodes with the use of Bézier curves. It comes with different color schemes (light/gray/dark mode), custom channel coloring, four node clustering algorithms which one can run on-the-fly, visualization in VR mode and predefined layer layouts (zig-zag, star and cube). This update also includes enhanced navigation controls (mouse orbit controls, layer dragging and layer/node selection), while its newly developed API allows integration with external applications as well as saving and loading of sessions in JSON format. Finally, a dedicated Cytoscape app has been developed, through which users can automatically send their 2D networks from Cytoscape to Arena3Dweb for 3D multi-layer visualization. Arena3Dweb is accessible at http://arena3d.pavlopouloslab.info or http://arena3d.org},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{zafeiropoulos_prego_2022,
title = {PREGO: A Literature and Data-Mining Resource to Associate Microorganisms, Biological Processes, and Environment Types},
author = {Haris Zafeiropoulos and Savvas Paragkamian and Stelios Ninidakis and Georgios A Pavlopoulos and Lars Juhl Jensen and Evangelos Pafilis},
url = {https://imbbc.hcmr.gr/wp-content/uploads/2022/03/2022-Zafeiropoulos-Micro-12.pdf
https://www.mdpi.com/2076-2607/10/2/293},
doi = {10.3390/microorganisms10020293},
issn = {2076-2607},
year = {2022},
date = {2022-01-01},
urldate = {2022-03-11},
journal = {Microorganisms},
volume = {10},
number = {2},
pages = {293},
abstract = {To elucidate ecosystem functioning, it is fundamental to recognize what processes occur in which environments (where) and which microorganisms carry them out (who). Here, we present PREGO, a one-stop-shop knowledge base providing such associations. PREGO combines text mining and data integration techniques to mine such what-where-who associations from data and metadata scattered in the scientific literature and in public omics repositories. Microorganisms, biological processes, and environment types are identified and mapped to ontology terms from established community resources. Analyses of comentions in text and co-occurrences in metagenomics data/metadata are performed to extract associations and a level of confidence is assigned to each of them thanks to a scoring scheme. The PREGO knowledge base contains associations for 364,508 microbial taxa, 1090 environmental types, 15,091 biological processes, and 7971 molecular functions with a total of almost 58 million associations. These associations are available through a web portal, an Application Programming Interface (API), and bulk download. By exploring environments and/or processes associated with each other or with microbes, PREGO aims to assist researchers in design and interpretation of experiments and their results. To demonstrate PREGO’s capabilities, a thorough presentation of its web interface is given along with a meta-analysis of experimental results from a lagoon-sediment study of sulfur-cycle related microbes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{paragkamian_automating_2022,
title = {Automating the Curation Process of Historical Literature on Marine Biodiversity Using Text Mining: The DECO Workflow},
author = {Savvas Paragkamian and Georgia Sarafidou and Dimitra Mavraki and Christina Pavloudi and Joana Beja and Menashè Eliezer and Marina Lipizer and Laura Boicenco and Leen Vandepitte and Ruben Perez-Perez and Haris Zafeiropoulos and Christos Arvanitidis and Evangelos Pafilis and Vasilis Gerovasileiou},
url = {https://imbbc.hcmr.gr/wp-content/uploads/2022/07/2022-Paragkaminan-fmars-53.pdf
},
doi = {10.3389/fmars.2022.940844},
issn = {2296-7745},
year = {2022},
date = {2022-01-01},
urldate = {2022-07-29},
journal = {Frontiers in Marine Science},
volume = {9},
pages = {940844},
abstract = {Historical biodiversity documents comprise an important link to the long-term data life cycle and provide useful insights on several aspects of biodiversity research and management. However, because of their historical context, they present specific challenges, primarily time- and effort-consuming in data curation. The data rescue process requires a multidisciplinary effort involving four tasks: (a) Document digitisation (b) Transcription, which involves text recognition and correction, and (c) Information Extraction, which is performed using text mining tools and involves the entity identification, their normalisation and their co-mentions in text. Finally, the extracted data go through (d) Publication to a data repository in a standardised format. Each of these tasks requires a dedicated multistep methodology with standards and procedures. During the past 8 years, Information Extraction (IE) tools have undergone remarkable advances, which created a landscape of various tools with distinct capabilities specific to biodiversity data. These tools recognise entities in text such as taxon names, localities, phenotypic traits and thus automate, accelerate and facilitate the curation process. Furthermore, they assist the normalisation and mapping of entities to specific identifiers. This work focuses on the IE step (c) from the marine historical biodiversity data perspective. It orchestrates IE tools and provides the curators with a unified view of the methodology; as a result the documentation of the strengths, limitations and dependencies of several tools was drafted. Additionally, the classification of tools into Graphical User Interface (web and standalone) applications and Command Line Interface ones enables the data curators to select the most suitable tool for their needs, according to their specific features. In addition, the high volume of already digitised marine documents that await curation is amassed and a demonstration of the methodology, with a new scalable, extendable and containerised tool, “DECO” (bioDivErsity data Curation programming wOrkflow) is presented. DECO’s usage will provide a solid basis for future curation initiatives and an augmented degree of reliability towards high value data products that allow for the connection between the past and the present, in marine biodiversity research.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{baltoumas_onthefly20_2021,
title = {OnTheFly2.0: a text-mining web application for automated biomedical entity recognition, document annotation, network and functional enrichment analysis},
author = {Fotis A Baltoumas and Sofia Zafeiropoulou and Evangelos Karatzas and Savvas Paragkamian and Foteini Thanati and Ioannis Iliopoulos and Aristides G Eliopoulos and Reinhard Schneider and Lars Juhl Jensen and Evangelos Pafilis and Georgios A Pavlopoulos},
url = {https://imbbc.hcmr.gr/wp-content/uploads/2021/12/2021-Baltoumas-NAR-74.pdf
https://academic.oup.com/nargab/article/doi/10.1093/nargab/lqab090/6382333},
doi = {10.1093/nargab/lqab090},
issn = {2631-9268},
year = {2021},
date = {2021-10-01},
urldate = {2021-12-01},
journal = {NAR Genomics and Bioinformatics},
volume = {3},
number = {4},
pages = {lqab090},
abstract = {Abstract
Extracting and processing information from documents is of great importance as lots of experimental results and findings are stored in local files. Therefore, extracting and analyzing biomedical terms from such files in an automated way is absolutely necessary. In this article, we present OnTheFly2.0, a web application for extracting biomedical entities from individual files such as plain texts, office documents, PDF files or images. OnTheFly2.0 can generate informative summaries in popup windows containing knowledge related to the identified terms along with links to various databases. It uses the EXTRACT tagging service to perform named entity recognition (NER) for genes/proteins, chemical compounds, organisms, tissues, environments, diseases, phenotypes and gene ontology terms. Multiple files can be analyzed, whereas identified terms such as proteins or genes can be explored through functional enrichment analysis or be associated with diseases and PubMed entries. Finally, protein–protein and protein–chemical networks can be generated with the use of STRING and STITCH services. To demonstrate its capacity for knowledge discovery, we interrogated published meta-analyses of clinical biomarkers of severe COVID-19 and uncovered inflammatory and senescence pathways that impact disease pathogenesis. OnTheFly2.0 currently supports 197 species and is available at http://bib.fleming.gr:3838/OnTheFly/ and http://onthefly.pavlopouloslab.info.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{zafeiropoulos_0s_2021,
title = {0s and 1s in marine molecular research: a regional HPC perspective},
author = {Haris Zafeiropoulos and Anastasia Gioti and Stelios Ninidakis and Antonis Potirakis and Savvas Paragkamian and Nelina Angelova and Aglaia Antoniou and Theodoros Danis and Eliza Kaitetzidou and Panagiotis Kasapidis and Jon Bent Kristoffersen and Vasileios Papadogiannis and Christina Pavloudi and Quoc Viet Ha and Jacques Lagnel and Nikos Pattakos and Giorgos Perantinos and Dimitris Sidirokastritis and Panagiotis Vavilis and Georgios Kotoulas and Tereza Manousaki and Elena Sarropoulou and Costas S Tsigenopoulos and Christos Arvanitidis and Antonios Magoulas and Evangelos Pafilis},
url = {https://imbbc.hcmr.gr/wp-content/uploads/2021/08/2021-Zafeiropoulos-GiGa-63.pdf
https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giab053/6353916},
doi = {10.1093/gigascience/giab053},
issn = {2047-217X},
year = {2021},
date = {2021-01-01},
urldate = {2021-08-23},
journal = {GigaScience},
volume = {10},
number = {8},
pages = {giab053},
abstract = {Abstract
High-performance computing (HPC) systems have become indispensable for modern marine research, providing support to an increasing number and diversity of users. Pairing with the impetus offered by high-throughput methods to key areas such as non-model organism studies, their operation continuously evolves to meet the corresponding computational challenges. Here, we present a Tier 2 (regional) HPC facility, operating for over a decade at the Institute of Marine Biology, Biotechnology, and Aquaculture of the Hellenic Centre for Marine Research in Greece. Strategic choices made in design and upgrades aimed to strike a balance between depth (the need for a few high-memory nodes) and breadth (a number of slimmer nodes), as dictated by the idiosyncrasy of the supported research. Qualitative computational requirement analysis of the latter revealed the diversity of marine fields, methods, and approaches adopted to translate data into knowledge. In addition, hardware and software architectures, usage statistics, policy, and user management aspects of the facility are presented. Drawing upon the last decade’s experience from the different levels of operation of the Institute of Marine Biology, Biotechnology, and Aquaculture HPC facility, a number of lessons are presented; these have contributed to the facility’s future directions in light of emerging distribution technologies (e.g., containers) and Research Infrastructure evolution. In combination with detailed knowledge of the facility usage and its upcoming upgrade, future collaborations in marine research and beyond are envisioned.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{fanini_coupling_2021,
title = {Coupling beach ecology and macroplastics litter studies: Current trends and the way ahead},
author = {Lucia Fanini and Omar Defeo and Michael Elliott and Savvas Paragkamian and Maurizio Pinna and Vanessa-Sarah Salvo},
url = {https://imbbc.hcmr.gr/wp-content/uploads/2021/12/Fanini-MPB-73-pre-print-1.pdf
https://linkinghub.elsevier.com/retrieve/pii/S0025326X21009851},
doi = {10.1016/j.marpolbul.2021.112951},
issn = {0025326X},
year = {2021},
date = {2021-01-01},
urldate = {2021-11-24},
journal = {Marine Pollution Bulletin},
volume = {173},
pages = {112951},
keywords = {},
pubstate = {published},
tppubtype = {article}
}