4 documents found, page 1 of 1
A toolbox of machine learning software to support microbiome analysis
Marcos-Zambrano, Laura Judith; López-Molina, Víctor Manuel; Bakir-Gungor, Burcu; Frohme, Marcus; Karaduzovic-Hadziabdic, Kanita; Klammsteiner, ThomasFunding Information: This study was supported by COST Action CA18131 “Statistical and machine learning techniques in human microbiome studies.” LM-Z is supported by Spanish State Research Agency Juan de la Cierva Grant IJC2019-042188-I (LM-Z). MB is supported by Metagenopolis grant ANR-11-DPBS-0001. MLC was partially supported by the Spanish Ministry of Economy, Industry and Competitiveness, Reference PID2019-1...
Overview of data preprocessing for machine learning applications in human micro...
Ibrahimi, Eliana; Lopes, Marta B.; Dhamo, Xhilda; Simeon, Andrea; Shigdel, Rajesh; Hron, Karel; Stres, Blaž; D’Elia, Domenica; Berland, MagaliThis article is based upon work from COST Action ML4Microbiome “Statistical and machine learning techniques in human microbiome studies” (CA18131), supported by COST (European Cooperation in Science and Technology), www.cost.eu .KH acknowledges support through the HiTEc Cost Action CA21163 and the project PID2021-123833OB-I00 provided by the Spanish Ministry of Science and Innovation (MCIN/AEI/10:13039/50110001...
Machine learning approaches in microbiome research
Papoutsoglou, Georgios; Tarazona, Sonia; Lopes, Marta B.; Klammsteiner, Thomas; Ibrahimi, Eliana; Eckenberger, Julia; Novielli, PierfrancescoFunding Information: We greatly thank Emmanuelle Le Chatelier and Pauline Barbet (Université Paris-Saclay, INRAE, MetaGenoPolis, 78350, Jouy-en-Josas, France) for preparing the shotgun CRC benchmark dataset. We also thank Michelangelo Ceci (Department of Computer Science, University of Bari Aldo Moro, Bari, Italy) and Christian Jansen (Institute of Science and Technology, Austria) for their interim leadership o...
Advancing microbiome research with machine learning
D’Elia, Domenica; Truu, Jaak; Lahti, Leo; Berland, Magali; Papoutsoglou, Georgios; Ceci, Michelangelo; Zomer, Aldert; Lopes, Marta B.; Ibrahimi, ElianaFunding Information: The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study is based upon work from COST Action ML4Microbiome “Statistical and machine learning techniques in human microbiome studies” (CA18131), supported by COST (European Cooperation in Science and Technology), www.cost.eu . MB acknowledges support through the Metagenopo...