Statistical and machine learning techniques in microbiome research
May 18 @ 15:15 – 16:15 CEST
Leo Lahti
Department of Computing, University of Turku, Turku, Finland
Leo Lahti is associate professor in data science in University of Turku, Finland. After completing his PhD in Aalto University, Finland, in 2010 he carried out several years of postdoctoral research in The Netherlands and Belgium on population studies of the human microbiome. Lahti has organized various international training events in microbiome bioinformatics, and he is the Finnish coordinator of the COST action on statistical and machine learning methods in microbiome studies.
Statistical and machine learning techniques in microbiome research
The diverse microbial communities living in human body have a profound influence on our well-being. Human microbiome research has expanded rapidly following the recent advances in high-throughput DNA sequencing and other omics’ technologies. Consequently, the demand for targeted computational methods has increased significantly in the recent years. We have a limited understanding of the overall mechanisms that control the observed variation and activity of these microbial ecosystems. Our observations have contributed to the systematic characterization of the individual dynamics and population variation of the human microbiome. I will discuss contemporary topics in statistical analysis and machine learning related to microbiome research, with a specific emphasis on probabilistic latent variable models in understanding the individual and dynamic variation across the landscape of microbiome composition.
Host: Anders Andersson
Read more about Leo Lahti´s research
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