Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/14055
Title: Literature on Applied Machine Learning in Metagenomic Classification: A Scoping Review
Authors: Tonkovic, Petar
Kalajdziski, Slobodan 
Zdravevski, Eftim 
Lameski, Petre 
Corizzo, Roberto
Pires, Ivan Miguel
Garcia, Nuno M.
Loncar-Turukalo, Tatjana
Trajkovikj, Vladimir 
Issue Date: 9-Dec-2020
Publisher: MDPI AG
Journal: Biology
Abstract: <jats:p>Applied machine learning in bioinformatics is growing as computer science slowly invades all research spheres. With the arrival of modern next-generation DNA sequencing algorithms, metagenomics is becoming an increasingly interesting research field as it finds countless practical applications exploiting the vast amounts of generated data. This study aims to scope the scientific literature in the field of metagenomic classification in the time interval 2008–2019 and provide an evolutionary timeline of data processing and machine learning in this field. This study follows the scoping review methodology and PRISMA guidelines to identify and process the available literature. Natural Language Processing (NLP) is deployed to ensure efficient and exhaustive search of the literary corpus of three large digital libraries: IEEE, PubMed, and Springer. The search is based on keywords and properties looked up using the digital libraries’ search engines. The scoping review results reveal an increasing number of research papers related to metagenomic classification over the past decade. The research is mainly focused on metagenomic classifiers, identifying scope specific metrics for model evaluation, data set sanitization, and dimensionality reduction. Out of all of these subproblems, data preprocessing is the least researched with considerable potential for improvement.</jats:p>
URI: http://hdl.handle.net/20.500.12188/14055
DOI: 10.3390/biology9120453
Appears in Collections:Faculty of Computer Science and Engineering: Journal Articles

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