RT Journal Article
T1 Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.
A1 Marcos-Zambrano, Laura Judith
A1 Karaduzovic-Hadziabdic, Kanita
A1 Loncar Turukalo, Tatjana
A1 Przymus, Piotr
A1 Trajkovik, Vladimir
A1 Aasmets, Oliver
A1 Berland, Magali
A1 Gruca, Aleksandra
A1 Hasic, Jasminka
A1 Hron, Karel
A1 Klammsteiner, Thomas
A1 Kolev, Mikhail
A1 Lahti, Leo
A1 Lopes, Marta B
A1 Moreno, Victor
A1 Naskinova, Irina
A1 Org, Elin
A1 Paciência, Inês
A1 Papoutsoglou, Georgios
A1 Shigdel, Rajesh
A1 Stres, Blaz
A1 Vilne, Baiba
A1 Yousef, Malik
A1 Zdravevski, Eftim
A1 Tsamardinos, Ioannis
A1 Carrillo de Santa Pau, Enrique
A1 Claesson, Marcus J
A1 Moreno-Indias, Isabel
A1 Truu, Jaak
K1 biomarker identification
K1 disease prediction
K1 feature selection
K1 machine learning
K1 microbiome
AB 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 and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.
SN 1664-302X
YR 2021
FD 2021-02-19
LK http://hdl.handle.net/10668/17365
UL http://hdl.handle.net/10668/17365
LA en
DS RISalud
RD Apr 5, 2025