Oliver Aasmets
University of Tartu
14 Papers
14 Citations
Oliver Aasmets is an academic researcher from University of Tartu. The author has contributed to research in topics: Microbiome & Biology. The author has an hindex of 2, co-authored 7 publications.
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Papers
Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.
Laura Judith Marcos-Zambrano,Kanita Karaduzovic-Hadziabdic,Tatjana Loncar Turukalo,Piotr Przymus,Vladimir Trajkovik,Oliver Aasmets,Magali Berland,Aleksandra Gruca,Jasminka Hasic,Karel Hron,Thomas Klammsteiner,Mikhail Kolev,Leo Lahti,Marta B. Lopes,Victor Moreno,Irina Naskinova,Elin Org,Inês Paciência,Georgios Papoutsoglou,Rajesh Shigdel,Blaz Stres,Baiba Vilne,Malik Yousef,Eftim Zdravevski,Ioannis Tsamardinos,Enrique Carrillo de Santa Pau,Marcus J. Claesson,Isabel Moreno-Indias,Isabel Moreno-Indias,Jaak Truu +29 more
TL;DR: In this paper, a review of the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities, is presented.
The Gut Microbiome in Polycystic Ovary Syndrome and its Association with Metabolic Traits.
Kreete Lüll,Riikka K Arffman,Alberto Sola-Leyva,Nerea M Molina,Oliver Aasmets,Karl-Heinz Herzig,Karl-Heinz Herzig,Julio Plaza-Díaz,Julio Plaza-Díaz,Stephen Franks,Laure Morin-Papunen,Juha S. Tapanainen,Juha S. Tapanainen,Andres Salumets,Signe Altmäe,Signe Altmäe,Terhi Piltonen,Elin Org +17 more
TL;DR: PCOS and non-PCOS women at late fertile age with similar BMI do not significantly differ in their gut microbial profiles, however, there are significant microbial changes in PCOS individuals depending on their metabolic health.
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Machine Learning Reveals Time-Varying Microbial Predictors with Complex Effects on Glucose Regulation.
Oliver Aasmets,Kreete Lüll,Jennifer M. Lang,Calvin Pan,Johanna Kuusisto,Krista Fischer,Markku Laakso,Aldons J. Lusis,Elin Org +8 more
- 16 Feb 2021
TL;DR: In this article, the authors used prospective data of 608 well-phenotyped Finnish men collected from the population-based Metabolic Syndrome in Men (METSIM) study to build machine learning models for predicting continuous glucose and insulin measures in a shorter (1.5) and longer (4) period.
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Machine learning reveals time-varying microbial predictors with complex effects on glucose regulation
Oliver Aasmets,Kreete Lüll,Jennifer M. Lang,Calvin Pan,Johanna Kuusisto,Krista Fischer,Markku Laakso,Aldons J. Lusis,Elin Org +8 more
TL;DR: This study is the first study that assesses the gut microbiome as a predictive measure for several type 2 diabetes associated parameters in a longitudinal study setting, and reveals a number of novel microbial biomarkers that can improve the prediction accuracy for continuous insulin measures and glycosylated hemoglobin levels.
14
Using fecal immunochemical tubes for the analysis of the gut microbiome has the potential to improve colorectal cancer screening.
TL;DR: In this article, the authors investigated the impact of FIT tubes and stabilization buffer on the microbial community structure evaluated in stool samples from 30 volunteers and compared the detected communities to those of fresh-frozen samples, highlighting previously published cancer-specific communities.