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Title: Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment
Author: Marcos Zambrano, Laura Judith
Karaduzovic-Hadziabdic, Kanita
Loncar Turukalo, Tatjana
Przymus, Piotr
Trajkovik, Vladimir
Aasmets, Oliver
Berland, Magali
Gruca, Aleksandra
Hasic, Jasminka
Hron, Karel
Klammsteiner, Thomas
Kolev, Mikhail
Lahti, Leo
Lopes, Marta B.
Moreno Aguado, Víctor
Naskinova, Irina
Org, Elin
Paciência, Inês
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
Keywords: Microbiota
Factors de risc en les malalties
Aprenentatge automàtic
Risk factors in diseases
Machine learning
Issue Date: 19-Feb-2021
Publisher: Frontiers Media S. A.
Abstract: 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.
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It is part of: Frontiers in Microbiology, 2021, vol. 12
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Appears in Collections:Articles publicats en revistes (Ciències Clíniques)
Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))

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