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 Trajkovik, Blanca Lacruz-Pleguezuelos, Oliver Aasmets, Ricardo Araujo, Ioannis Anagnostopoulos, Önder Aydemir, Magali Berland, M Luz Calle, Michelangelo Ceci, Hatice Duman, Aycan Gündoğdu, 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 Susín, Ines Thiele, Ciprian-Octavian Truică, Paul Wilmes, Ercument Yilmaz, Malik Yousef, Marcus Joakim Claesson, Jaak Truu, Enrique Carrillo de Santa Pau
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...
2023: Frontiers in Microbiology