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D’Elia D, Truu J, Lahti L, Berland M, Papoutsoglou G, Ceci M, Zomer A, Lopes MB, Ibrahimi E, Gruca A, Nechyporenko A, Frohme M, Klammsteiner T, Pau ECDS, Marcos-Zambrano LJ, Hron K, Pio G, Simeon A, Suharoschi R, Moreno-Indias I, Temko A, Nedyalkova M, Apostol ES, Truică CO, Shigdel R, Telalović JH, Bongcam-Rudloff E, Przymus P, Jordamović NB, Falquet L, Tarazona S, Sampri A, Isola G, Pérez-Serrano D, Trajkovik V, Klucar L, Loncar-Turukalo T, Havulinna AS, Jansen C, Bertelsen RJ, Claesson MJ. Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action. Front Microbiol 2023; 14:1257002. [PMID: 37808321 PMCID: PMC10558209 DOI: 10.3389/fmicb.2023.1257002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 09/05/2023] [Indexed: 10/10/2023] Open
Abstract
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.
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Affiliation(s)
- Domenica D’Elia
- Department of Biomedical Sciences, National Research Council, Institute for Biomedical Technologies, Bari, Italy
| | - Jaak Truu
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
| | - Magali Berland
- Université Paris-Saclay, INRAE, MetaGenoPolis, Jouy-en-Josas, France
| | - Georgios Papoutsoglou
- JADBio Gnosis DA S.A., Science and Technology Park of Crete, Heraklion, Greece
- Department of Computer Science, University of Crete, Heraklion, Greece
| | - Michelangelo Ceci
- Department of Computer Science, University of Bari Aldo Moro, Bari, Italy
| | - Aldert Zomer
- Department of Biomolecular Health Sciences (Infectious Diseases and Immunology), Faculty of Veterinary Medicine, Utrecht University, Utrecht, Netherlands
| | - Marta B. Lopes
- Center for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology, Caparica, Portugal
- UNIDEMI, Department of Mechanical and Industrial Engineering, NOVA School of Science and Technology, Caparica, Portugal
| | - Eliana Ibrahimi
- Department of Biology, University of Tirana, Tirana, Albania
| | - Aleksandra Gruca
- Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, Poland
| | - Alina Nechyporenko
- Systems Engineering Department, Kharkiv National University of Radio Electronics, Kharkiv, Ukraine
- Department of Molecular Biotechnology and Functional Genomics, Technical University of Applied Sciences Wildau, Wildau, Germany
| | - Marcus Frohme
- Department of Molecular Biotechnology and Functional Genomics, Technical University of Applied Sciences Wildau, Wildau, Germany
| | - Thomas Klammsteiner
- Department of Microbiology, Universität Innsbruck, Innsbruck, Austria
- Department of Ecology, Universität Innsbruck, Innsbruck, Austria
| | - Enrique Carrillo-de Santa Pau
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, CEI UAM+CSIC, Madrid, Spain
| | - Laura Judith Marcos-Zambrano
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, CEI UAM+CSIC, Madrid, Spain
| | - Karel Hron
- Department of Mathematical Analysis and Applications of Mathematics, Faculty of Science, Palacký University, Olomouc, Czechia
| | - Gianvito Pio
- Department of Computer Science, University of Bari Aldo Moro, Bari, Italy
| | - Andrea Simeon
- BioSense Institute, University of Novi Sad, Novi Sad, Serbia
| | - Ramona Suharoschi
- Molecular Nutrition and Proteomics Research Laboratory, Department of Food Science, University of Agricultural Sciences and Veterinary Medicine of Cluj-Napoca, Cluj-Napoca, Romania
| | - Isabel Moreno-Indias
- Department of Endocrinology and Nutrition, Virgen de la Victoria University Hospital, the Biomedical Research Institute of Malaga and Platform in Nanomedicine (IBIMA-BIONAND Platform), University of Malaga, Malaga, Spain
| | - Andriy Temko
- Department of Electrical and Electronic Engineering, University College Cork, Cork, Ireland
| | | | - Elena-Simona Apostol
- Computer Science and Engineering Department, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania
| | - Ciprian-Octavian Truică
- Computer Science and Engineering Department, Faculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania
| | - Rajesh Shigdel
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Jasminka Hasić Telalović
- Department of Computer Science, University Sarajevo School of Science and Technology, Sarajevo, Bosnia and Herzegovina
| | - Erik Bongcam-Rudloff
- Swedish University of Agricultural Sciences, Department of Animal Breeding and Genetics, Uppsala, Sweden
| | | | - Naida Babić Jordamović
- Computational Biology, International Centre for Genetic Engineering and Biotechnology, Trieste, Italy
- Verlab Research Institute for BIomedical Engineering, Medical Devices and Artificial Intelligence, Sarajevo, Bosnia and Herzegovina
| | - Laurent Falquet
- University of Fribourg and Swiss Institute of Bioinformatics, Fribourg, Switzerland
| | - Sonia Tarazona
- Department of Applied Statistics and Operations Research and Quality, Universitat Politècnica de València, València, Spain
| | - Alexia Sampri
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom
| | - Gaetano Isola
- Department of General Surgery and Surgical-Medical Specialties, School of Dentistry, University of Catania, Catania, Italy
| | - David Pérez-Serrano
- Computational Biology Group, Precision Nutrition and Cancer Research Program, IMDEA Food Institute, CEI UAM+CSIC, Madrid, Spain
| | | | - Lubos Klucar
- Institute of Molecular Biology, Slovak Academy of Sciences, Bratislava, Slovakia
| | | | - Aki S. Havulinna
- Finnish Institute for Health and Welfare, Helsinki, Finland
- Institute for Molecular Medicine Finland, FIMM-HiLIFE, Helsinki, Finland
| | - Christian Jansen
- Biome Diagnostics GmbH, Vienna, Austria
- Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria
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Chumachenko D, Butkevych M, Lode D, Frohme M, Schmailzl KJG, Nechyporenko A. Machine Learning Methods in Predicting Patients with Suspected Myocardial Infarction Based on Short-Time HRV Data. Sensors (Basel) 2022; 22:7033. [PMID: 36146381 PMCID: PMC9502529 DOI: 10.3390/s22187033] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 09/15/2022] [Accepted: 09/15/2022] [Indexed: 06/16/2023]
Abstract
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.
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Affiliation(s)
- Dmytro Chumachenko
- Mathematical Modelling and Artificial Intelligence Department, National Aerospace University Kharkiv Aviation Institute, 61072 Kharkiv, Ukraine
- Molecular Biotechnology and Functional Genomics Department, Technical University of Applied Sciences Wildau, 15745 Wildau, Germany
| | - Mykola Butkevych
- Mathematical Modelling and Artificial Intelligence Department, National Aerospace University Kharkiv Aviation Institute, 61072 Kharkiv, Ukraine
| | - Daniel Lode
- Molecular Biotechnology and Functional Genomics Department, Technical University of Applied Sciences Wildau, 15745 Wildau, Germany
| | - Marcus Frohme
- Molecular Biotechnology and Functional Genomics Department, Technical University of Applied Sciences Wildau, 15745 Wildau, Germany
| | | | - Alina Nechyporenko
- Molecular Biotechnology and Functional Genomics Department, Technical University of Applied Sciences Wildau, 15745 Wildau, Germany
- Systems Engineering Department, Kharkiv National University of Radio Electronics, 61166 Kharkiv, Ukraine
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Pfeil J, Nechyporenko A, Frohme M, Hufert FT, Schulze K. Examination of blood samples using deep learning and mobile microscopy. BMC Bioinformatics 2022; 23:65. [PMID: 35148679 PMCID: PMC8832798 DOI: 10.1186/s12859-022-04602-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 02/04/2022] [Indexed: 12/27/2022] Open
Abstract
Background Microscopic examination of human blood samples is an excellent opportunity to assess general health status and diagnose diseases. Conventional blood tests are performed in medical laboratories by specialized professionals and are time and labor intensive. The development of a point-of-care system based on a mobile microscope and powerful algorithms would be beneficial for providing care directly at the patient's bedside. For this purpose human blood samples were visualized using a low-cost mobile microscope, an ocular camera and a smartphone. Training and optimisation of different deep learning methods for instance segmentation are used to detect and count the different blood cells. The accuracy of the results is assessed using quantitative and qualitative evaluation standards. Results Instance segmentation models such as Mask R-CNN, Mask Scoring R-CNN, D2Det and YOLACT were trained and optimised for the detection and classification of all blood cell types. These networks were not designed to detect very small objects in large numbers, so extensive modifications were necessary. Thus, segmentation of all blood cell types and their classification was feasible with great accuracy: qualitatively evaluated, mean average precision of 0.57 and mean average recall of 0.61 are achieved for all blood cell types. Quantitatively, 93% of ground truth blood cells can be detected. Conclusions Mobile blood testing as a point-of-care system can be performed with diagnostic accuracy using deep learning methods. In the future, this application could enable very fast, cheap, location- and knowledge-independent patient care.
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Affiliation(s)
- Juliane Pfeil
- Molecular Biology and Functional Genomics, Technical University of Applied Sciences, Hochschulring 1, 15745, Wildau, Germany
| | - Alina Nechyporenko
- Molecular Biology and Functional Genomics, Technical University of Applied Sciences, Hochschulring 1, 15745, Wildau, Germany.,Kharkiv National University of Radio Electronics, Kharkiv, Ukraine
| | - Marcus Frohme
- Molecular Biology and Functional Genomics, Technical University of Applied Sciences, Hochschulring 1, 15745, Wildau, Germany.
| | - Frank T Hufert
- Institute for Microbiology and Virology, Brandenburg Medical School Theodor Fontane, Neuruppin, Germany
| | - Katja Schulze
- Oculyze GmbH, Mobile Microscopy and Computer Vision, Wildau, Germany
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Hollmann S, Kremer A, Baebler Š, Trefois C, Gruden K, Rudnicki WR, Tong W, Gruca A, Bongcam-Rudloff E, Evelo CT, Nechyporenko A, Frohme M, Šafránek D, Regierer B, D'Elia D. The need for standardisation in life science research - an approach to excellence and trust. F1000Res 2020; 9:1398. [PMID: 33604028 PMCID: PMC7863991 DOI: 10.12688/f1000research.27500.2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/21/2021] [Indexed: 11/20/2022] Open
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.
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Affiliation(s)
- Susanne Hollmann
- Faculty of Science, University of Potsdam, Potsdam, Brandenburg, 14476, Germany.,SB Science Management UG (Haftungsbeschränkt), Berlin, Berlin, 12163, Germany
| | - Andreas Kremer
- Information Technology for Translational Medicine S.A. ITTM S.A., Esch-sur-Alzette, Esch, 4354, Luxembourg
| | - Špela Baebler
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, 1000, Slovenia
| | - Christophe Trefois
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, 4367, Luxembourg
| | - Kristina Gruden
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, 1000, Slovenia
| | - Witold R Rudnicki
- Institute of Computer Science, University of Białystok, Białystok, 15-328, Poland
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, Jefferson, USA
| | - Aleksandra Gruca
- Department of Computer Networks and Systems, Silesian University of Technology, Gliwice, 44-100, Poland
| | - Erik Bongcam-Rudloff
- Department of Animal Breeding and Genetics, Bioinformatics section, University of Agricultural Sciences, Uppsala, 750 07, Sweden
| | - Chris T Evelo
- Department of Bioinformatics - BiGCaT, Maastricht University, Maastricht, 6229 ER, The Netherlands.,Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, 6229 ER, The Netherlands
| | - Alina Nechyporenko
- Department of Systems Engineering, Kharkiv National University of Radio Electronics, Kharkiv Oblast, 61000, Ukraine
| | - Marcus Frohme
- Division Molecular Biotechnology and Functional Genomics, Technical University of Applied Sciences Wildau, Wildau, Brandenburg, 15745, Germany
| | | | - Babette Regierer
- SB Science Management UG (Haftungsbeschränkt), Berlin, Berlin, 12163, Germany.,Leibniz Institute of Vegetable and Ornamental Crops (IGZ), Großbeeren, Brandenburg, 14979, Germany
| | - Domenica D'Elia
- Institute for Biomedical Technologies, National Research Council, Italy, Bari, 70126, Italy
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Vogt K, Bachmann-Harildstad G, Lintermann A, Nechyporenko A, Peters F, Wernecke KD. The new agreement of the international RIGA consensus conference on nasal airway function tests. Rhinology 2018; 56:133-143. [PMID: 29353289 DOI: 10.4193/rhin17.084] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The report reflects an agreement based on the consensus conference of the International Standardization Committee on the Objective Assessment of the Nasal Airway in Riga, 2nd Nov. 2016. The aim of the conference was to address the existing nasal airway function tests and to take into account physical, mathematical and technical correctness as a base of international standardization as well as the requirements of the Council Directive 93/42/EEC of 14 June 1993 concerning medical devices. Rhinomanometry, acoustic rhinometry, peak nasal inspiratory flow, Odiosoft-Rhino, optical rhinometry, 24-h measurements, computational fluid dynamics, nasometry and the mirrow test were evaluated for important diagnostic criteria, which are the precision of the equipment including calibration and the software applied; validity with sensitivity, specificity, positive and negative predictive values, reliability with intra-individual and inter-individual reproducibility and responsiveness in clinical studies. For rhinomanometry, the logarithmic effective resistance was set as the parameter of high diagnostic relevance. In acoustic rhinometry, the area of interest for the minimal cross-sectional area will need further standardization. Peak nasal inspiratory flow is a reproducible and fast test, which showed a high range of mean values in different studies. The state of the art with computational fluid dynamics for the simulation of the airway still depends on high performance computing hardware and will, after standardization of the software and both the software and hardware for imaging protocols, certainly deliver a better understanding of the nasal airway flux.
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Affiliation(s)
- K Vogt
- University of Latvia, Center of Experimental Surgery, Riga, Latvia
| | - G Bachmann-Harildstad
- Department of otorhinolaryngology, Akershus University Hospital and Institute of Clinical Medicine, Akershus University Hospital, Oslo University, Lorenskog, Norway
| | - A Lintermann
- Institute of Aerodynamics and Chair of Fluid Mechanics, RWTH Aachen University, Aachen, Germany
| | - A Nechyporenko
- Kharkiv National University of Radio Electronics, Department of Biomedical Engineering, Kharkiv, Ukraine
| | - F Peters
- Ruhr-Universitat Bochum, Mechanical Engineering, Fluid Mechanics, Bochum, Germany
| | - K D Wernecke
- Charite-Universitatsmedizin Berlin, Berlin, Germany
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