TY - JOUR A1 - D'Elia, Domenica A1 - Truu, Jaak A1 - Lahti, Leo A1 - Berland, Magali A1 - Papoutsoglou, Georgios A1 - Ceci, Michelangelo A1 - Zomer, Aldert A1 - Lopes, Marta B. A1 - Ibrahimi, Eliana A1 - Gruca, Aleksandra A1 - Nechyporenko, Alina A1 - Frohme, Marcus A1 - Klammsteiner, Thomas A1 - Carrillo de Santa Pau, Enrique A1 - Marcos-Zambrano, Laura Judith A1 - Hron, Karel A1 - Pio, Gianvito A1 - Simeon, Andrea A1 - Suharoschi, Ramona A1 - Moreno-Indias, Isabel A1 - Temko, Andriy A1 - Nedyalkova, Miroslava A1 - Apostol, Elena-Simona A1 - Truică, Ciprian-Octavian A1 - Shigdel, Rajesh A1 - Telalović, Jasminka Hasić A1 - Bongcam-Rudloff, Erik A1 - Przymus, Piotr A1 - Jordamović, Naida Babić A1 - Falquet, Laurent A1 - Tarazona, Sonia A1 - Sampri, Alexia A1 - Isola, Gaetano A1 - Pérez-Serrano, David A1 - Trajkovik, Vladimir A1 - Klucar, Lubos A1 - Loncar-Turukalo, Tatjana A1 - Havulinna, Aki S. A1 - Jansen, Christian A1 - Bertelsen, Randi J. A1 - Claesson, Marcus Joakim T1 - Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action JF - Frontiers in Microbiology N2 - The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish “gold standard” protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory ‘omics’ features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices. KW - microbiome KW - machine learning KW - artificial intelligence KW - standard KW - best practice Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-18004 SN - 1664-302X VL - 14 PB - Frontiers ER - TY - JOUR A1 - Marcos-Zambrano, Laura Judith A1 - López-Molina, Víctor Manuel A1 - Bakir-Gungor, Burcu A1 - Frohme, Marcus A1 - Karaduzovic-Hadziabdic, Kanita A1 - Klammsteiner, Thomas A1 - Ibrahimi, Eliana A1 - Lahti, Leo A1 - Loncar-Turukalo, Tatjana A1 - Dhamo, Xhilda A1 - Simeon, Andrea A1 - Nechyporenko, Alina A1 - Pio, Gianvito A1 - Przymus, Piotr A1 - Sampri, Alexia A1 - Trajkovik, Vladimir A1 - Lacruz-Pleguezuelos, Blanca A1 - Aasmets, Oliver A1 - Araujo, Ricardo A1 - Anagnostopoulos, Ioannis A1 - Aydemir, Önder A1 - Berland, Magali A1 - Calle, M. Luz A1 - Ceci, Michelangelo A1 - Duman, Hatice A1 - Gündoğdu, Aycan A1 - Havulinna, Aki S. A1 - Kaka Bra, Kardokh Hama Najib A1 - Kalluci, Eglantina A1 - Karav, Sercan A1 - Lode, Daniel A1 - Lopes, Marta B. A1 - May, Patrick A1 - Nap, Bram A1 - Nedyalkova, Miroslava A1 - Paciência, Inês A1 - Pasic, Lejla A1 - Pujolassos, Meritxell A1 - Shigdel, Rajesh A1 - Susín, Antonio A1 - Thiele, Ines A1 - Truică, Ciprian-Octavian A1 - Wilmes, Paul A1 - Yilmaz, Ercument A1 - Yousef, Malik A1 - Claesson, Marcus Joakim A1 - Truu, Jaak A1 - Carrillo de Santa Pau, Enrique T1 - A toolbox of machine learning software to support microbiome analysis JF - Frontiers in Microbiology N2 - 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. KW - microbiome KW - machine learning KW - software KW - feature generation KW - feature analysis KW - data integration KW - microbial gene prediction KW - microbial metabolic modeling Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-18271 UR - https://www.frontiersin.org/articles/10.3389/fmicb.2023.1250806/ SN - 1664-302X VL - 14 PB - Frontiers ER - TY - JOUR A1 - Alekseeva, Victoriia A1 - Nechyporenko, Alina A1 - Frohme, Marcus A1 - Gargin, Vitaliy A1 - Meniailov, Ievgen A1 - Chumachenko, Dmytro T1 - Intelligent Decision Support System for Differential Diagnosis of Chronic Odontogenic Rhinosinusitis Based on U-Net Segmentation JF - Electronics N2 - The share of chronic odontogenic rhinosinusitis is 40% among all chronic rhinosinusitis. Using automated information systems for differential diagnosis will improve the efficiency of decision-making by doctors in diagnosing chronic odontogenic rhinosinusitis. Therefore, this study aimed to develop an intelligent decision support system for the differential diagnosis of chronic odontogenic rhinosinusitis based on computer vision methods. A dataset was collected and processed, including 162 MSCT images. A deep learning model for image segmentation was developed. A 23 convolutional layer U-Net network architecture has been used for the segmentation of multi-spiral computed tomography (MSCT) data with odontogenic maxillary sinusitis. The proposed model is implemented in such a way that each pair of repeated 3 × 3 convolutions layers is followed by an Exponential Linear Unit instead of a Rectified Linear Unit as an activation function. The model showed an accuracy of 90.09%. To develop a decision support system, an intelligent chatbot allows the user to conduct an automated patient survey and collect patient examination data from several doctors of various profiles. The intelligent information system proposed in this study made it possible to combine an image processing model with a patient interview and examination data, improving physician decision-making efficiency in the differential diagnosis of Chronic Odontogenic Rhinosinusitis. The proposed solution is the first comprehensive solution in this area. KW - CT data KW - deep learning KW - image segmentation KW - U-Net KW - decision support system KW - chronic odontogenic rhinosinusitis KW - machine learning KW - artificial intelligence Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-17088 SN - 2079-9292 VL - 12 IS - 5 PB - MDPI ER - TY - JOUR A1 - Chumachenko, Dmytro A1 - Butkevych, Mykola A1 - Lode, Daniel A1 - Frohme, Marcus A1 - Schmailzl, Kurt J. G. A1 - Nechyporenko, Alina T1 - Machine Learning Methods in Predicting Patients with Suspected Myocardial Infarction Based on Short-Time HRV Data JF - Sensors N2 - Diagnosis of cardiovascular diseases is an urgent task because they are the main cause of death for 32% of the world’s population. Particularly relevant are automated diagnostics using machine learning methods in the digitalization of healthcare and introduction of personalized medicine in healthcare institutions, including at the individual level when designing smart houses. Therefore, this study aims to analyze short 10-s electrocardiogram measurements taken from 12 leads. In addition, the task is to classify patients with suspected myocardial infarction using machine learning methods. We have developed four models based on the k-nearest neighbor classifier, radial basis function, decision tree, and random forest to do this. An analysis of time parameters showed that the most significant parameters for diagnosing myocardial infraction are SDNN, BPM, and IBI. An experimental investigation was conducted on the data of the open PTB-XL dataset for patients with suspected myocardial infarction. The results showed that, according to the parameters of the short ECG, it is possible to classify patients with a suspected myocardial infraction as sick and healthy with high accuracy. The optimized Random Forest model showed the best performance with an accuracy of 99.63%, and a root mean absolute error is less than 0.004. The proposed novel approach can be used for patients who do not have other indicators of heart attacks. KW - myocardial infraction KW - heart rate variability KW - 10-second heart rate variability KW - diagnostics KW - machine learning KW - k-nearest neighbor classifier KW - radial basis function KW - decision tree KW - random forest Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-16521 SN - 1424-8220 VL - 22 IS - 18 PB - MDPI ER - TY - JOUR A1 - Nechyporenko, Alina A1 - Frohme, Marcus A1 - Strelchuk, Yaroslav A1 - Omelchenko, Vladyslav A1 - Gargin, Vitaliy A1 - Ishchenko, Liudmyla A1 - Alekseeva, Victoriia T1 - Galvanic Skin Response and Photoplethysmography for Stress Recognition Using Machine Learning and Wearable Sensors JF - Applied Sciences N2 - This study investigates stress recognition using galvanic skin response (GSR) and photoplethysmography (PPG) data and machine learning, with a new focus on air raid sirens as a stressor. It bridges laboratory and real-world conditions and highlights the reliability of wearable sensors in dynamic, high-stress environments such as war and conflict zones. The study involves 37 participants (20 men, 17 women), aged 20–30, who had not previously heard an air raid siren. A 70 dB “S-40 electric siren” (400–450 Hz) was delivered via headphones. The protocol included a 5 min resting period, followed by 3 min “no-stress” phase, followed by 3 min “stress” phase, and finally a 3 min recovery phase. GSR and PPG signals were recorded using Shimmer 3 GSR+ sensors on the fingers and earlobes. A single session was conducted to avoid sensitization. The workflow includes signal preprocessing to remove artifacts, feature extraction, feature selection, and application of different machine learning models to classify the “stress “and “no-stress” states. As a result, the best classification performance was shown by the k-Nearest Neighbors model, achieving 0.833 accuracy. This was achieved by using a particular combination of heart rate variability (HRV) and GSR features, which can be considered as new indicators of siren-induced stress. KW - galvanic skin response KW - machine learning KW - photoplethysmography KW - stress Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19803 VL - 14 IS - 24 PB - MDPI ER - TY - CHAP A1 - Chumachenko, Dmytro A1 - Nechyporenko, Alina A1 - Frohme, Marcus ED - Shakhovska, Nataliya ED - Chrétien, Stéphane ED - Izonin, Ivan ED - Campos, Jaime T1 - Impact of Russian War on COVID-19 Dynamics in Germany: the Simulation Study by Statistical Machine Learning T2 - Proceedings of the 5th International Conference on Informatics & Data-Driven Medicine, Lyon, France, November 18 - 20, 2022 N2 - 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. KW - epidemic model KW - machine learning KW - polynomial regression KW - war KW - COVID-19 KW - infectious disease simulation Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19074 UR - https://ceur-ws.org/Vol-3302/short4.pdf VL - 3302 SP - 78 EP - 85 PB - RWTH Aachen ER -