- , , , , , , , , , , , , , , , , , , , , , , , ,
- Eliana Ibrahimi, Marta B. Lopes, Xhilda Dhamo, Andrea Simeon, Rajesh Shigdel, Karel Hron, Blaž Stres, Domenica D’Elia, Magali Berland and Laura Judith Marcos Zambrano. Overview of Data Preprocessing for Machine Learning Applications in Human Microbiome Research. Front. Microbiol., Sec. Systems Microbiology, Volume 14 – 2023 | doi: 10.3389/fmicb.2023.1250909
- Domenica D’Elia, Jaak Truu, Leo Lahti, Magali Berland, Giorgos Papoutsoglou, Michelangelo Ceci, Aldert Zomer, Marta B Lopes, Eliana Ibrahimi, Aleksandra Gruca, Alina Nechyporenko, Marcus Frohme, Thomas Klammsteiner, Enrique Carrillo De Santa Pau, Laura Judith Marcos Zambrano, Karel Hron, Gianvito Pio, Andrea Simeon, Ramona Suharoschi, Isabel Moreno Indias, Andriy Temko, Miroslava Nedyalkova, Elena-Simona Apostol, Ciprian-Octavian Truică, Rajesh Shigdel, Jasminka Hasic Telalovic, Erik Bongcam-Rudloff, Piotr Przymus, Naida Babić Jordamović, Laurent Falquet, Sonia Tarazona, Alexia Sampri, Gaetano Isola, David Pérez-Serrano, Vladimir Tihomir Trajkovik, Lubos Klucar, Tatjana Loncar-Turukalo, Aki S Havulinna, Christian Jansen, Randi Jacobsen Bertelsen and Marcus Claesson (2023) Advancing Microbiome Research with Machine Learning: Key Findings from the ML4Microbiome COST Action. Front. Microbiol., Sec. Systems Microbiology, Volume 14. doi: 10.3389/fmicb.2023.1250806
- Laura Judith Marcos Zambrano, Víctor Manuel López Molina, Burcu Bakir-Gungor, Marcus Frohme, Kanita Karaduzovic-Hadziabdic, Thomas Klammsteiner, Eliana Ibrahimi, Leo Lahti, Tatjana Loncar-Turukalo, Xhilda Dhamo, Andrea Simeon, Alina Nechyporenko, Gianvito Pio, Piotr Przymus, Alexia Sampri, Vladimir Tihomir Trajkovik, Oliver Aasmets, Ricardo Araujo, Ioannis Anagnostopoulos, Onder Aydemir, Magali Berland, María de la Luz Calle, Michelangelo Ceci, Hatice Duman, Aycan Gundogdu, Aki S. Havulinna, Kardokh Hama Najib Kaka Bra, Eglantina Kalluci, Sercan Karav, Daniel Lode, Marta B Lopes, Patrick May, Bram Nap, Miroslava Nedyalkova, Inês Paciência, Lejla Pasic, Meritxell Pujolassos, Rajesh Shigdel, Antonio Susin, Ines Thiele, Ciprian-Octavian Truică, Paul Wilmes, Ercüment Yılmaz, Malik Yousef, Marcus Joakim Claesson, Jaak Truu and Enrique Carrillo De Santa Pau (2023) A toolbox of machine learning software to support microbiome analysis. Front. Microbiol. Sec. Systems Microbiology. Volume 14. doi: 10.3389/fmicb.2023.1250806
- Georgios Papoutsoglou, Sonia Tarazona, Marta B Lopes, Thomas Klammsteiner, Eliana Ibrahimi, Julia Eckenberger, Pierfrancesco Novielli, Alberto Tonda, Andrea Simeon, Rajesh Shigdel, Stéphane Béreux, Giacomo Vitali, Sabina Tangaro, Leo Lahti, Andriy Temko, Marcus Claesson and Magali Berland (2023) Machine Learning Approaches in Microbiome Research: Challenges and Best Practices. Front. Microbiol. Sec. Systems Microbiology, Volume 14. doi: 10.3389/fmicb.2023.1261889
- Eliana Ibrahimi, Mina Norouzirad, Melisa Meto and Marta B. Lopes. 2023. Regularized Generalized Linear Models to Disclose Host-Microbiome Associations in Colorectal Cancer. In Proceedings of 2023 6th International Conference on Mathematics and Statistics (ICoMS 2023), July 14-16, 2023, Leipzig, Germany. ACM, New York, NY, USA, 7 Pages. https://doi.org/10.
1145/3613347.3613362 - López-Molina VM, Lacruz-Pleguezuelos B., Carrillo de Santa Pau E., Marcos-Zambrano LJ. Uncovering the link between gut microbiome, highly processed food consumption and diet quality through bioinformatics methods. bioRxiv 2023.03.07.530223; doi: https://doi.org/10.1101/2023.03.07.530223
- Pfeil J, Siptroth J, Pospisil H, Frohme M, Hufert FT, Moskalenko O, Yateem M, Nechyporenko A. Classification of Microbiome Data from Type 2 Diabetes Mellitus Individuals with Deep Learning Image Recognition. Big Data and Cognitive Computing. 2023; 7(1):51. https://doi.org/10.3390/bdcc7010051
- Arcila-Galvis JE, Loria-Kohen V, Ramírez de Molina A, Carrillo de Santa Pau E, Marcos-Zambrano LJ. A comprehensive map of microbial biomarkers along the gastrointestinal tract for celiac disease patients. Front Microbiol. 2022 Sep 13;13:956119. doi: 10.3389/fmicb.2022.956119.
- Lacruz-Pleguezuelos N., Fernández LP, Ramírez de Molina A., Carrillo de Santa Pau E.,Marcos-Zambrano LJ. Bioinformatic methods for stratification of obese patients and identification of cancer susceptibility biomarkers based on the analysis of the gut microbiome. bioRxiv 2022.11.17.516892; doi: https://doi.org/10.1101/2022.11.17.516892
- Obón-Santacana, M.; Mas-Lloret, J.; Bars-Cortina, D.; Criado-Mesas, L.; Carreras-Torres, R.; Díez-Villanueva, A.; Moratalla-Navarro, F.; Guinó, E.; Ibáñez-Sanz, G.; Rodríguez-Alonso, L.; Mulet-Margalef, N.; Mata, A.; García-Rodríguez, A.; Duell, E.J.; Pimenoff, V.N.; Moreno, V. Meta-Analysis and Validation of a Colorectal Cancer Risk Prediction Model Using Deep Sequenced Fecal Metagenomes. Cancers 2022, 14, 4214. https://doi.org/10.3390/cancers14174214
- Frontiers in Genetics volume – Research Topic: Microbiome and Machine Learning (2022) – 10 articles published. Microbiome and Machine Learning | Frontiers Research Topic (frontiersin.org)
- Moreno-Indias I., Zomer AL, Gómez-Cabrero D, Claesson MJ on behalf of ML4Microbiome (2022) Editorial: Microbiome and Machine Learning Research Topic. Front. Microbiol., Sec. Evolutionary and Genomic Microbiology. https://doi.org/10.3389/fmicb.2022.964921
- Cekikj Miodrag, Jakimovska Özdemir, Milena Kalajdzhiski, Slobodan Özcan, Orhan Sezerman, Osman Uğur (2022) Understanding the Role of the Microbiome in Cancer Diagnostics and Therapeutics by Creating and Utilizing ML Models. Appl. Sci.12(9), 4094; https://doi.org/10.3390/app12094094
- Vilne Baiba, Ķibilds Juris, Siksna Inese, Lazda Ilva, Valciņa Olga, Krūmiņa Angelika (2022) Could Artificial Intelligence/Machine Learning and Inclusion of Diet-Gut Microbiome Interactions Improve Disease Risk Prediction? Case Study: Coronary Artery Disease. Frontiers in Microbiology, 13. doi:10.3389/fmicb.2022.627892
- Ibrahimi, E., Elbere, I., Berland, M., & D’Elia, D. (2022) Report of the ML4Microbiome workshop 2021 – Statistical and Machine Learning Techniques for Microbiome Data Analysis. EMBnet.journal, 27, e1012. doi:https://doi.org/10.14806/ej.27.0.1012
- Rosario D, Bidkhori G, Lee S, Bedarf J, Hildebrand F, Le Chatelier E, Uhlen M, Ehrlich SD, Proctor G, Wüllner U, Mardinoglu A, Shoaie S. Systematic analysis of gut microbiome reveals the role of bacterial folate and homocysteine metabolism in Parkinson’s disease. Cell Rep. 2021 Mar 2;34(9):108807. doi: 10.1016/j.celrep.2021.108807. PMID: 33657381.
- Loncar-Turukalo, T., Claesson, M., Bertelsen, R., Zomer, A., & D’Elia, D. (2021). Towards the optimisation and standardisation of Machine Learning techniques for human microbiome research: the ML4Microbiome COST Action (CA 18131). EMBnet.journal, 26(A), e997. doi:https://doi.org/10.14806/ej.26.A.997
- Gholamreza Bidkhori, Sunjae Lee, LindseyA. Edwards, Emmanuelle Le Chatelier, Mathieu Almeida, Bouchra Ezzamouri, Florian Plaza Onate, Nicolas Ponte, Debbie L. Shawcross, Gordon Proctor, Lars Nielsen, Jens Nielsen, Mathias Uhlen, Stanislav Dusko Ehrlich, Saeed Shoaie (2021) The Reactobiome Unravels a New Paradigm in Human Gut Microbiome Metabolism.
- Shoaie S, Lee S, Almeida M, et al. (2021) Global and temporal state of the human gut microbiome in health and disease. Research Square. doi:10.21203/rs.3.rs-339282/v1
- Marcos-Zambrano LJ, Karaduzovic-Hadziabdic K, Loncar Turukalo T, Przymus P, Trajkovik V, Aasmets O, Berland M, Gruca A, Hasic J, Hron K, Klammsteiner T, Kolev M, Lahti L, Lopes MB, Moreno V, Naskinova I, Org E, Paciência I, Papoutsoglou G, Shigdel R, Stres B, Vilne B, Yousef M, Zdravevski E, Tsamardinos I, Carrillo de Santa Pau E, Claesson MJ, Moreno-Indias I, Truu J. Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment. Front Microbiol. 2021 Feb 19;12:634511. doi: 10.3389/fmicb.2021.634511. PMID: 33737920; PMCID: PMC7962872.
- Moreno-Indias I, Lahti L, Nedyalkova M, Elbere I, Roshchupkin G, Adilovic M, Aydemir O, Bakir-Gungor B, Santa Pau EC, D’Elia D, Desai MS, Falquet L, Gundogdu A, Hron K, Klammsteiner T, Lopes MB, Marcos-Zambrano LJ, Marques C, Mason M, May P, Pašić L, Pio G, Pongor S, Promponas VJ, Przymus P, Saez-Rodriguez J, Sampri A, Shigdel R, Stres B, Suharoschi R, Truu J, Truică CO, Vilne B, Vlachakis D, Yilmaz E, Zeller G, Zomer AL, Gómez-Cabrero D, Claesson MJ. Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions. Front Microbiol. 2021 Feb 22;12:635781. doi: 10.3389/fmicb.2021.635781. PMID: 33692771; PMCID: PMC7937616.
- Tonkovic P, Kalajdziski S, Zdravevski E, Lameski P, Corizzo R, Pires IM, Garcia NM, Loncar-Turukalo T, Trajkovik V. Literature on Applied Machine Learning in Metagenomic Classification: A Scoping Review. Biology (Basel). 2020 Dec 9;9(12):453. doi: 10.3390/biology9120453. PMID: 33316921; PMCID: PMC7763105.
COST (European Cooperation in Science and Technology) is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and
enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation (COST | European Cooperation in Science and Technology)
Address
Grant holder institution:
(GH Manager: Dr Anna Power)
Biosciences Institute,
University College Cork,
Western road,
Cork, Ireland,
T12 YT20.