@inproceedings{ReshetnikMuryzinaFrohmeetal.2024, author = {Reshetnik, Viktor and Muryzina, Irina and Frohme, Marcus and Alekseeva, Victoriia and Dzyza, Alla and Nechyporenko, Alina}, title = {Uncertainty Estimation Method for Determining Bone Density in Patients with Infiltrating Intraductal Carcinoma Undergoing Anti-Cancer Therapy}, series = {Proceedings of the 4th International Workshop of IT-professionals on Artificial Intelligence (ProfIT AI 2024) 2024, Cambridge, MA, USA, September 25-27, 2024}, booktitle = {Proceedings of the 4th International Workshop of IT-professionals on Artificial Intelligence (ProfIT AI 2024) 2024, Cambridge, MA, USA, September 25-27, 2024}, number = {3777}, editor = {Chumachenko, Dmytro and Golland, Polina and Yakovlev, Sergiy}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-19628}, pages = {222 -- 228}, year = {2024}, abstract = {Identification of a bone density is crucial in all branches of medicine. Assessing bone density is a labor-intensive process. Bone density is quite unstable and depends on many factors, both physiological (aging, hormonal balance) and pathological (medication use, various underlying conditions). The aim of our study is to determine bone density in patients with malignant breast tumors undergoing anti-cancer therapy. Material and Methods. The study included 50 women aged 60-70 years who were diagnosed with infiltrating intraductal carcinoma. According to established protocols, MSCT is recommended for this category of women at intervals of once every six months. The first MSCT scan was performed immediately after the diagnosis was made, before the start of treatment, and the second scan was conducted six months later. Results. The maximum radiological density was 75.8954±37.9477 Hu in the group of women who had been receiving treatment for six months, compared to 93.9388±46.9694 Hu in the group of patients who did not take the drug. Meanwhile, the minimum density showed a slight increase from 29.7295±14.8647 Hu to 38.6919±19.3460 Hu, which can be attributed to the compensatory mechanisms of the body. Conclusions. In the course of this study, bone density in patients with infiltrating intraductal carcinoma undergoing anti-cancer therapy was determined using uncertainty estimation. It was found that after six months, the first to respond to changes in density was the maximum bone density.}, language = {en} } @inproceedings{NechyporenkoHubarenkoHubarenkoetal.2022, author = {Nechyporenko, Alina and Hubarenko, Yevhen and Hubarenko, Maryna and Kalnytska, Violeta and Alekseeva, Victoriia and Gargin, Vitaliy}, title = {Automatic Creation of Masks for Marking Histological Images of the Epithelium of the Paranasal Sinuses}, series = {Proceedings of the 5th International Conference on Informatics \& Data-Driven Medicine, Lyon, France, November 18 - 20, 2022}, volume = {3302}, booktitle = {Proceedings of the 5th International Conference on Informatics \& Data-Driven Medicine, Lyon, France, November 18 - 20, 2022}, editor = {Shakhovska, Nataliya and Chr{\´e}tien, St{\´e}phane and Izonin, Ivan and Campos, Jaime}, publisher = {RWTH Aachen}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-19024}, pages = {108 -- 119}, year = {2022}, abstract = {The article discusses the approach to solving the problem of reducing time spent on the preparation of medical images for teaching neural networks, by reducing the time of creating masks for images. The task is considered on the example of processing images of the mucous membrane of the paranasal sinus. The specifics of the task did not allow effectively using existing software solutions. During the study, a software solution was proposed, which made it possible to radically reduce the time of creating masks for images. The article also analyzes the shortcomings of the automated creation of masks, as well as the directions of their solution. The loss of time due to the adjustment of the color palette can be reduced even more to 1-2 minutes, the average deviation is 7.61\%.}, language = {en} } @inproceedings{NechyporenkoReshetnikDzyzaetal.2023, author = {Nechyporenko, Alina and Reshetnik, Viktor and Dzyza, Alla and Alekseeva, Victoriia and Lupyr, Andrii and Gargin, Vitaliy}, title = {The Peculiarities of Measuring Bone Density in Males and Females Using Uncertainty Calculation}, series = {Proceedings of the 6th International Conference on Informatics \& Data-Driven Medicine, Bratislava, Slovakia, November 17-19, 2023}, volume = {3609}, booktitle = {Proceedings of the 6th International Conference on Informatics \& Data-Driven Medicine, Bratislava, Slovakia, November 17-19, 2023}, editor = {Shakhovska, Nataliya and Kov{\´a}c, Michal and Izonin, Ivan and Chr{\´e}tien, St{\´e}phane}, publisher = {RWTH Aachen}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-19116}, pages = {249 -- 254}, year = {2023}, abstract = {The article discusses the approach to solving the problem of reducing time spent on the preparation of medical images for teaching neural networks, by reducing the time of creating masks for images. The task is considered on the example of processing images of the mucous membrane of the paranasal sinus. The specifics of the task did not allow effectively using existing software solutions. During the study, a software solution was proposed, which made it possible to radically reduce the time of creating masks for images. The article also analyzes the shortcomings of the automated creation of masks, as well as the directions of their solution. The loss of time due to the adjustment of the color palette can be reduced even more to 1-2 minutes, the average deviation is 7.61\%.}, language = {en} } @inproceedings{NechyporenkoReshetnikShyianetal.2020, author = {Nechyporenko, Alina and Reshetnik, Viktor and Shyian, Denys and Alekseeva, Victoriia and Radutny, Radiy and Gargin, Vitaliy}, title = {Solutions to the 3D Model Problem of Pressure Measurement in the Area of Maxillary Sinus Anastomosis}, series = {Proceedings of the 3rd International Conference on Informatics \& Data-Driven Medicine}, booktitle = {Proceedings of the 3rd International Conference on Informatics \& Data-Driven Medicine}, editor = {Shakhovska, Nataliya and Campos, Jaime and Melnykova, Nataliia and Izonin, Ivan}, publisher = {RWTH Aachen}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-19007}, pages = {275 -- 284}, year = {2020}, abstract = {The ostiomeatal complex (OMC) is a key area that determines the occurrence of inflammatory processes in the paranasal sinuses (PNSs). The aim of our work was to develop a procedure for studying the OMC components in the preoperative period that allows for identification the impact of anatomical peculiarities on change of physiological pressure in the maxillary sinus. Materials and methods: The study was carried out on the basis of the otorhinolaryngological department of Kharkiv Regional Clinical Hospital in 2019-2020. It involved 100 patients of both sexes aged 20-59 years with chronic non-polyposis maxillary sinusitis. Results: The sizes of the uncinate process, the middle turbinate and the natural anastomosis were determined using the calculation of uncertainty. Basing on the data obtained, all the patients were divided into three groups. Conclusions: Changes in the size of the natural anastomosis (both an increase and its narrowing) lead to changes in pressure in the area of the anastomosis, and a decrease in ventilation in the paranasal sinuses. SCT study with subsequent 3D modeling is an informative, accurate and effective method for assessment of OMC and PNSs condition. It allows surgeons to presume the method and volume of surgery as early as at the preoperative stage, without resorting to invasive research methods.}, language = {en} } @inproceedings{StrelchukNechyporenkoFrohmeetal.2024, author = {Strelchuk, Yaroslav and Nechyporenko, Alina and Frohme, Marcus and Gargin, Vitaliy and Lupyr, Andrii and Alekseeva, Victoriia}, title = {Detection of stress using photoplethysmography}, series = {Proceedings of the 7th International Conference on Informatics \& Data-Driven Medicine (IDDM 2024)}, volume = {3892}, booktitle = {Proceedings of the 7th International Conference on Informatics \& Data-Driven Medicine (IDDM 2024)}, editor = {Shakhovska, Nataliya and Jiao, Jianbo and Izonin, Ivan and Chretien, Stephane}, publisher = {RWTH Aachen}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-19896}, pages = {196 -- 202}, year = {2024}, abstract = {Stress is an important factor affecting human health, and its timely detection can significantly improve quality of life. This study addresses the current issue of identifying stress states using photoplethysmography signals obtained from the wearable Shimmer 3 sensor. The purpose of the study was to establish an effective approach for stress detection, specifically through the analysis of heart rate variability changes during different states: rest and stress. A distinctive feature of the research is the use of the air raid siren sound as a stress factor. The research methodology includes the collection and analysis of biosignals, allowing for the assessment of cardiovascular system functioning under the impact of stress factors. The results of the study demonstrated significant differences in heart rhythm indicators depending on the person's state, highlighting the potential of these metrics as a tool for health monitoring and stress detection. The paper proposes a new approach to using photoplethysmography for assessing stress responses, which may contribute to the development of personalized stress management methods.}, language = {en} } @inproceedings{RadutnyNechyporenkoAlekseevaetal.2021, author = {Radutny, Radiy and Nechyporenko, Alina and Alekseeva, Victoriia and Yurevych, Nadiia and Lupyr, Andrii and Gargin, Vitaliy}, title = {Complex Automatic Evaluation of the Medical Images of the Paranasal Sinuses}, series = {Proceedings of the 4th International Conference on Informatics \& Data-Driven Medicine, Valencia, Spain, November 19 - 21, 2021}, booktitle = {Proceedings of the 4th International Conference on Informatics \& Data-Driven Medicine, Valencia, Spain, November 19 - 21, 2021}, editor = {Shakhovska, Nataliya and Salazar, Addisson and Izonin, Ivan and Campos, Jaime}, publisher = {RWTH Aachen}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-19010}, pages = {182 -- 189}, year = {2021}, abstract = {Evaluation of medical images is of key importance in the work of medical staff today. Especially this problem pays important role in otolaryngology. The aim of our work was to develop an automatic complex method for assessing the state of the human paranasal sinuses. Our research included 10 people of different sex and age, who were divided into groups, taking into account the recommendations of the WHO for 2019-2021. The structure of the mucous membrane of the maxillary sinus were calculated and compared. In the course of our research, an algorithm was developed for the automatic assessment of the state of the mucous membrane of the maxillary sinus and its bone walls according to the data of the spiral computed tomography. The difference between obtained data in the manual and automatic mode is minimal.}, language = {en} } @inproceedings{NechyporenkoFrohmeOmelchenkoetal.2023, author = {Nechyporenko, Alina and Frohme, Marcus and Omelchenko, Vladyslav and Alekseeva, Victoriia and Lupyr, Andrii and Gargin, Vitaliy}, title = {Identification of Personality Based on the Sphenoid Sinus Structure Using Machine Learning}, series = {Proceedings of the 6th International Conference on Informatics \& Data-Driven Medicine, Bratislava, Slovakia, November 17-19, 2023}, volume = {3609}, booktitle = {Proceedings of the 6th International Conference on Informatics \& Data-Driven Medicine, Bratislava, Slovakia, November 17-19, 2023}, editor = {Shakhovska, Nataliya and Kov{\´a}c, Michal and Izonin, Ivan and Chr{\´e}tien, St{\´e}phane}, publisher = {CEUR-WS.org}, url = {http://nbn-resolving.de/urn:nbn:de:0074-3609-1}, pages = {167 -- 173}, year = {2023}, abstract = {The aim of our study is to develop a new, simple, and effective method for identification of personality based on the characteristics of the sphenoid sinus structure, using machine learning for subsequent implementation into routine medical practice in Ukraine. The study involved 200 multislice computed tomography (MSCT) scans of individuals of various genders and ages. During the study, we obtained results with an accuracy exceeding 70\%.}, language = {en} } @inproceedings{ChumachenkoNechyporenkoFrohme2022, author = {Chumachenko, Dmytro and Nechyporenko, Alina and Frohme, Marcus}, title = {Impact of Russian War on COVID-19 Dynamics in Germany: the Simulation Study by Statistical Machine Learning}, series = {Proceedings of the 5th International Conference on Informatics \& Data-Driven Medicine, Lyon, France, November 18 - 20, 2022}, volume = {3302}, booktitle = {Proceedings of the 5th International Conference on Informatics \& Data-Driven Medicine, Lyon, France, November 18 - 20, 2022}, editor = {Shakhovska, Nataliya and Chr{\´e}tien, St{\´e}phane and Izonin, Ivan and Campos, Jaime}, publisher = {RWTH Aachen}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-19074}, pages = {78 -- 85}, year = {2022}, abstract = {The new coronavirus COVID-19 has been spreading worldwide for almost three years. The global community has developed effective measures to contain and control the pandemic. However, new factors are emerging that are driving the dynamics of COVID-19. One of these factors was the escalation of Russia's war in Ukraine. This study aims to test the hypothesis of the influence of migration flows caused by the Russian war in Ukraine on the dynamics of the epidemic process in Germany. For this, a model of the COVID-19 epidemic process was built based on the polynomial regression method. The model's adequacy was tested 30 days before the start of the escalation of the Russian war in Ukraine. To assess the impact of the war on the dynamics of COVID-19, the model was used to calculate the forecast of cumulative new and fatal cases of COVID-19 in Germany in the first 30 days after the start of the escalation of the Russian war in Ukraine. Modeling showed that migration flows from Ukraine are not a critical factor in the growth of the dynamics of the incidence of COVID-19 in Germany, but they influenced the number of cases. The next stage of the study is the development of more complex models for a detailed analysis of population dynamics, identifying factors influencing the epidemic process in the context of the Russian war in Ukraine, and assessing their information content.}, language = {en} } @unpublished{HollmannKremerBaebleretal.2020, author = {Hollmann, Susanne and Kremer, Andreas and Baebler, Špela and Trefois, Christophe and Gruden, Kristina and Rudnicki, Witold R. and Tong, Weida and Gruca, Aleksandra and Bongcam-Rudloff, Erik and Evelo, Chris T. and Nechyporenko, Alina and Frohme, Marcus and Šafr{\´a}nek, David and Regierer, Babette and D'Elia, Domenica}, title = {The need for standardisation in life science research - an approach to excellence and trust. [version 1; peer review: 3 approved]}, series = {F1000Research}, volume = {9}, journal = {F1000Research}, issn = {2046-1402}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-13891}, year = {2020}, abstract = {Today, academic researchers benefit from the changes driven by digital technologies and the enormous growth of knowledge and data, on globalisation, enlargement of the scientific community, and the linkage between different scientific communities and the society. To fully benefit from this development, however, information needs to be shared openly and transparently. Digitalisation plays a major role here because it permeates all areas of business, science and society and is one of the key drivers for innovation and international cooperation. To address the resulting opportunities, the EU promotes the development and use of collaborative ways to produce and share knowledge and data as early as possible in the research process, but also to appropriately secure results with the European strategy for Open Science (OS). It is now widely recognised that making research results more accessible to all societal actors contributes to more effective and efficient science; it also serves as a boost for innovation in the public and private sectors. However for research data to be findable, accessible, interoperable and reusable the use of standards is essential. At the metadata level, considerable efforts in standardisation have already been made (e.g. Data Management Plan and FAIR Principle etc.), whereas in context with the raw data these fundamental efforts are still fragmented and in some cases completely missing. The CHARME consortium, funded by the European Cooperation in Science and Technology (COST) Agency, has identified needs and gaps in the field of standardisation in the life sciences and also discussed potential hurdles for implementation of standards in current practice. Here, the authors suggest four measures in response to current challenges to ensure a high quality of life science research data and their re-usability for research and innovation.}, language = {en} } @article{HollmannFrohmeEndrullatetal.2020, author = {Hollmann, Susanne and Frohme, Marcus and Endrullat, Christoph and Kremer, Andreas and D'Elia, Domenica and Regierer, Babette and Nechyporenko, Alina}, title = {Ten simple rules on how to write a standard operating procedure}, series = {PLoS Computational Biology}, volume = {16}, journal = {PLoS Computational Biology}, number = {9}, issn = {1553-7358}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-13598}, pages = {e1008095}, year = {2020}, abstract = {Research publications and data nowadays should be publicly available on the internet and, theoretically, usable for everyone to develop further research, products, or services. The long-term accessibility of research data is, therefore, fundamental in the economy of the research production process. However, the availability of data is not sufficient by itself, but also their quality must be verifiable. Measures to ensure reuse and reproducibility need to include the entire research life cycle, from the experimental design to the generation of data, quality control, statistical analysis, interpretation, and validation of the results. Hence, high-quality records, particularly for providing a string of documents for the verifiable origin of data, are essential elements that can act as a certificate for potential users (customers). These records also improve the traceability and transparency of data and processes, therefore, improving the reliability of results. Standards for data acquisition, analysis, and documentation have been fostered in the last decade driven by grassroot initiatives of researchers and organizations such as the Research Data Alliance (RDA). Nevertheless, what is still largely missing in the life science academic research are agreed procedures for complex routine research workflows. Here, well-crafted documentation like standard operating procedures (SOPs) offer clear direction and instructions specifically designed to avoid deviations as an absolute necessity for reproducibility. Therefore, this paper provides a standardized workflow that explains step by step how to write an SOP to be used as a starting point for appropriate research documentation.}, language = {en} } @article{MarcosZambranoLopezMolinaBakirGungoretal.2023, author = {Marcos-Zambrano, Laura Judith and L{\´o}pez-Molina, V{\´i}ctor Manuel and Bakir-Gungor, Burcu and Frohme, Marcus and Karaduzovic-Hadziabdic, Kanita and Klammsteiner, Thomas and Ibrahimi, Eliana and Lahti, Leo and Loncar-Turukalo, Tatjana and Dhamo, Xhilda and Simeon, Andrea and Nechyporenko, Alina and Pio, Gianvito and Przymus, Piotr and Sampri, Alexia and Trajkovik, Vladimir and Lacruz-Pleguezuelos, Blanca and Aasmets, Oliver and Araujo, Ricardo and Anagnostopoulos, Ioannis and Aydemir, {\"O}nder and Berland, Magali and Calle, M. Luz and Ceci, Michelangelo and Duman, Hatice and G{\"u}ndoğdu, Aycan and Havulinna, Aki S. and Kaka Bra, Kardokh Hama Najib and Kalluci, Eglantina and Karav, Sercan and Lode, Daniel and Lopes, Marta B. and May, Patrick and Nap, Bram and Nedyalkova, Miroslava and Paci{\^e}ncia, In{\^e}s and Pasic, Lejla and Pujolassos, Meritxell and Shigdel, Rajesh and Sus{\´i}n, Antonio and Thiele, Ines and Truică, Ciprian-Octavian and Wilmes, Paul and Yilmaz, Ercument and Yousef, Malik and Claesson, Marcus Joakim and Truu, Jaak and Carrillo de Santa Pau, Enrique}, title = {A toolbox of machine learning software to support microbiome analysis}, series = {Frontiers in Microbiology}, volume = {14}, journal = {Frontiers in Microbiology}, publisher = {Frontiers}, issn = {1664-302X}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-18271}, year = {2023}, abstract = {The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.}, language = {en} } @article{PfeilSiptrothPospisiletal.2023, author = {Pfeil, Juliane and Siptroth, Julienne and Pospisil, Heike and Frohme, Marcus and Hufert, Frank T. and Moskalenko, Olga and Yateem, Murad and Nechyporenko, Alina}, title = {Classification of Microbiome Data from Type 2 Diabetes Mellitus Individuals with Deep Learning Image Recognition}, series = {Big Data and Cognitive Computing}, volume = {7}, journal = {Big Data and Cognitive Computing}, number = {1}, publisher = {MDPI}, issn = {2504-2289}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-17184}, year = {2023}, abstract = {Microbiomic analysis of human gut samples is a beneficial tool to examine the general well-being and various health conditions. The balance of the intestinal flora is important to prevent chronic gut infections and adiposity, as well as pathological alterations connected to various diseases. The evaluation of microbiome data based on next-generation sequencing (NGS) is complex and their interpretation is often challenging and can be ambiguous. Therefore, we developed an innovative approach for the examination and classification of microbiomic data into healthy and diseased by visualizing the data as a radial heatmap in order to apply deep learning (DL) image classification. The differentiation between 674 healthy and 272 type 2 diabetes mellitus (T2D) samples was chosen as a proof of concept. The residual network with 50 layers (ResNet-50) image classification model was trained and optimized, providing discrimination with 96\% accuracy. Samples from healthy persons were detected with a specificity of 97\% and those from T2D individuals with a sensitivity of 92\%. Image classification using DL of NGS microbiome data enables precise discrimination between healthy and diabetic individuals. In the future, this tool could enable classification of different diseases and imbalances of the gut microbiome and their causative genera.}, language = {en} } @article{KhrutbaNikitchenkoBalykinetal.2025, author = {Khrutba, Viktoriia and Nikitchenko, Yuliia and Balykin, Ivan and Nechyporenko, Alina and Frohme, Marcus}, title = {Academic \& educational networking as a mechanism for the resilience of university cooperation in the digital era}, series = {European Journal of Interdisciplinary Issues}, volume = {2}, journal = {European Journal of Interdisciplinary Issues}, number = {2}, publisher = {East European Association of Scientists}, address = {Warszaw}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-20957}, pages = {84 -- 97}, year = {2025}, abstract = {The article investigates the evolution of digital educational networks as instruments of resilience and sustainability within German-Ukrainian higher education cooperation. Its primary objective is to analyze how academic networking contributes to maintaining educational continuity, fostering innovation, and strengthening institutional adaptability under crisis conditions. The study focuses on two representative initiatives, the Digital Teaching Network (DTN) and the Wildau-Kharkiv IT Bridge, which exemplify the transition from traditional digital platforms to interconnected network ecosystems. Methodologically, the research combines quantitative and qualitative approaches, including an online survey, focus group discussions, and content analysis of responses from 185 participants representing 21 universities in both countries. This mixed-method design enables a comprehensive understanding of the motivational, organizational, and social determinants of participation in educational networks. The analysis identifies four main categories of network actors, initiators, active collaborators, occasional participants, and observers, whose interaction defines the internal dynamics and resilience of such networks. The study's findings demonstrate that networking serves as a key mechanism for sustaining academic interaction, enhancing educators' digital competencies, and facilitating interdisciplinary collaboration. It further shows that niche educational networks, those focused on specific thematic or professional domains, play an increasingly important role in advancing specialized expertise and fostering trust-based partnerships beyond the lifespan of individual projects. The scientific novelty of the article lies in conceptualizing the shift from digital platforms as technical tools to educational networks as socio-institutional ecosystems. Practically, the results highlight the potential of network-based cooperation to support the sustainable development, modernization, and internationalization of Ukrainian higher education in the digital era.}, language = {en} }