Blar i Department of Clinical Science på forfatter "Lahti, Leo"

Viser treff 1-5 av 5

    • Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action 

      D’Elia, Domenica; Truu, Jaak; Lahti, Leo; Berland, Magali; Papoutsoglou, Georgios; Ceci, Michelangelo; Zomer, Aldert; Lopes, Marta B.; Ibrahimi, Eliana; Gruca, Aleksandra; Nechyporenko, Alina; Frohme, Marcus; Klammsteiner, Thomas; Pau, Enrique Carrillo-de Santa; Marcos-Zambrano, Laura Judith; Hron, Karel; Pio, Gianvito; Simeon, Andrea; Suharoschi, Ramona; Moreno-Indias, Isabel; Temko, Andriy; Nedyalkova, Miroslava; Apostol, Elena-Simona; Truică, Ciprian-Octavian; Shigdel, Rajesh; Telalović, Jasminka Hasić; Bongcam-Rudloff, Erik; Przymus, Piotr; Jordamović, Naida Babić; Falquet, Laurent; Tarazona, Sonia; Sampri, Alexia; Isola, Gaetano; Pérez-Serrano, David; Trajkovik, Vladimir; Klucar, Lubos; Loncar-Turukalo, Tatjana; Havulinna, Aki S.; Jansen, Christian; Bertelsen, Randi Jacobsen; Claesson, Marcus Joakim (Journal article; Peer reviewed, 2023)
      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 ...

    • Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment 

      Marcos-Zambrano, Laura Judith; Hadziabdic, Kanita Karaduzovic; Turukalo, Tatjana Loncar; Przymus, Piotr; Trajkovik, Vladimir; Aasmets, Oliver; Berland, Magali; Gruca, Aleksandra; Hasic, Jasminka; Hron, Karel; Klammsteiner, Thomas; Kolev, Mikhail; Lahti, Leo; Lopes, Marta B.; Moreno, Victor; Naskinova, Irina; Org, Elin; Paciencia, Ines; Papoutsoglou, Georgios; Shigdel, Rajesh; Stres, Blaz; Vilne, Baiba; Yousef, Malik; Zdravevski, Eftim; Tsamardinos, Ioannis; Carrillo de Santa Pau, Enrique; Claesson, Marcus J.; Moreno-Indias, Isabel; Truu, Jaak (Journal article; Peer reviewed, 2021)
      The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations ...

    • Machine learning approaches in microbiome research: challenges and best practices 

      Papoutsoglou, Georgios; Tarazona, Sonia; Lopes, Marta B.; Klammsteiner, Thomas; Ibrahimi, Eliana; Eckenberger, Julia; Novielli, Pierfrancesco; Tonda, Alberto; Simeon, Andrea; Shigdel, Rajesh; Béreux, Stéphane; Vitali, Giacomo; Tangaro, Sabina; Lahti, Leo; Temko, Andriy; Claesson, Marcus J.; Berland, Magali (Journal article; Peer reviewed, 2023)
      Microbiome data predictive analysis within a machine learning (ML) workflow presents numerous domain-specific challenges involving preprocessing, feature selection, predictive modeling, performance estimation, model ...

    • Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions 

      Moreno-Indias, Isabel; Lahti, Leo; Nedyalkova, Miroslava; Elbere, Ilze; Roshchupkin, Gennady V.; Adilovic, Muhamed; Aydemir, Onder; Bakir-Gungor, Burcu; Carrillo-de Santa Pau, Enrique; D’Elia, Domenica; Desai, Mahesh S.; Falquet, Laurent; Gundogdu, Aycan; Hron, Karel; Klammsteiner, Thomas; Lopes, Marta B.; Marcos-Zambrano, Laura Judith; Marques, Cláudia; Mason, Michael; May, Patrick; Pašić, Lejla; Pio, Gianvito; Pongor, Sándor; Promponas, Vasilis J.; Przymus, Piotr; Saez-Rodriguez, Julio; Sampri, Alexia; Shigdel, Rajesh; Stres, Blaz; Suharoschi, Ramona; Truu, Jaak; Truică, Ciprian-Octavian; Vilne, Baiba; Vlachakis, Dimitrios; Yilmaz, Ercument; Zeller, Georg; Zomer, Aldert L.; Gomez-Cabrero, David; Claesson, Marcus J. (Journal article; Peer reviewed, 2021)
      The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many ...

    • 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, Thomas; Ibrahimi, Eliana; Lahti, Leo; Loncar-Turukalo, Tatjana; Dhamo, Xhilda; Simeon, Andrea; Nechyporenko, Alina; Pio, Gianvito; Przymus, Piotr; Sampri, Alexia; Trajkovik, Vladimir; Lacruz-Pleguezuelos, Blanca; Aasmets, Oliver; Araujo, Ricardo; Anagnostopoulos, Ioannis; Aydemir, Önder; Berland, Magali; Calle, M. Luz; Ceci, Michelangelo; Duman, Hatice; Gündoğdu, Aycan; Havulinna, Aki S.; Kaka Bra, Kardokh Hama Najib; Kalluci, Eglantina; Karav, Sercan; Lode, Daniel; Lopes, Marta B.; May, Patrick; Nap, Bram; Nedyalkova, Miroslava; Paciência, Inês; Pasic, Lejla; Pujolassos, Meritxell; Shigdel, Rajesh; Susín, Antonio; Thiele, Ines; Truică, Ciprian-Octavian; Wilmes, Paul; Yilmaz, Ercument; Yousef, Malik; Claesson, Marcus Joakim; Truu, Jaak; Carrillo de Santa Pau, Enrique (Journal article; Peer reviewed, 2023)
      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 ...