Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/14055
DC FieldValueLanguage
dc.contributor.authorTonkovic, Petaren_US
dc.contributor.authorKalajdziski, Slobodanen_US
dc.contributor.authorZdravevski, Eftimen_US
dc.contributor.authorLameski, Petreen_US
dc.contributor.authorCorizzo, Robertoen_US
dc.contributor.authorPires, Ivan Miguelen_US
dc.contributor.authorGarcia, Nuno M.en_US
dc.contributor.authorLoncar-Turukalo, Tatjanaen_US
dc.contributor.authorTrajkovikj, Vladimiren_US
dc.date.accessioned2021-07-06T09:56:20Z-
dc.date.available2021-07-06T09:56:20Z-
dc.date.issued2020-12-09-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/14055-
dc.description.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>en_US
dc.language.isoenen_US
dc.publisherMDPI AGen_US
dc.relation.ispartofBiologyen_US
dc.titleLiterature on Applied Machine Learning in Metagenomic Classification: A Scoping Reviewen_US
dc.typeJournal Articleen_US
dc.identifier.doi10.3390/biology9120453-
dc.identifier.urlhttps://www.mdpi.com/2079-7737/9/12/453/pdf-
dc.identifier.volume9-
dc.identifier.issue12-
item.grantfulltextopen-
item.fulltextWith Fulltext-
crisitem.author.deptFaculty of Computer Science and Engineering-
crisitem.author.deptFaculty of Computer Science and Engineering-
crisitem.author.deptFaculty of Computer Science and Engineering-
crisitem.author.deptFaculty of Computer Science and Engineering-
Appears in Collections:Faculty of Computer Science and Engineering: Journal Articles
Show simple item record

Page view(s)

32
checked on Aug 14, 2022

Download(s)

9
checked on Aug 14, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.