Literature on Applied Machine Learning in Metagenomic Classification: A Scoping Review
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 exploi...
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doaj-8d9540fbb55f4bf0b9eb128ecebc52302020-12-10T00:00:52ZengMDPI AGBiology2079-77372020-12-01945345310.3390/biology9120453Literature on Applied Machine Learning in Metagenomic Classification: A Scoping ReviewPetar Tonkovic0Slobodan Kalajdziski1Eftim Zdravevski2Petre Lameski3Roberto Corizzo4Ivan Miguel Pires5Nuno M. Garcia6Tatjana Loncar-Turukalo7Vladimir Trajkovik8Faculty of Computer Science and Engineering, Saints Cyril and Methodius University, 1000 Skopje, MacedoniaFaculty of Computer Science and Engineering, Saints Cyril and Methodius University, 1000 Skopje, MacedoniaFaculty of Computer Science and Engineering, Saints Cyril and Methodius University, 1000 Skopje, MacedoniaFaculty of Computer Science and Engineering, Saints Cyril and Methodius University, 1000 Skopje, MacedoniaDepartment of Computer Science, American University, Washington, DC 20016, USAInstituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, PortugalInstituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, PortugalFaculty of Technical Sciences, University of Novi Sad, 21102 Novi Sad, SerbiaFaculty of Computer Science and Engineering, Saints Cyril and Methodius University, 1000 Skopje, MacedoniaApplied 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.https://www.mdpi.com/2079-7737/9/12/453metagenomicsscoping reviewclassificationdata preprocessing |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Petar Tonkovic Slobodan Kalajdziski Eftim Zdravevski Petre Lameski Roberto Corizzo Ivan Miguel Pires Nuno M. Garcia Tatjana Loncar-Turukalo Vladimir Trajkovik |
spellingShingle |
Petar Tonkovic Slobodan Kalajdziski Eftim Zdravevski Petre Lameski Roberto Corizzo Ivan Miguel Pires Nuno M. Garcia Tatjana Loncar-Turukalo Vladimir Trajkovik Literature on Applied Machine Learning in Metagenomic Classification: A Scoping Review Biology metagenomics scoping review classification data preprocessing |
author_facet |
Petar Tonkovic Slobodan Kalajdziski Eftim Zdravevski Petre Lameski Roberto Corizzo Ivan Miguel Pires Nuno M. Garcia Tatjana Loncar-Turukalo Vladimir Trajkovik |
author_sort |
Petar Tonkovic |
title |
Literature on Applied Machine Learning in Metagenomic Classification: A Scoping Review |
title_short |
Literature on Applied Machine Learning in Metagenomic Classification: A Scoping Review |
title_full |
Literature on Applied Machine Learning in Metagenomic Classification: A Scoping Review |
title_fullStr |
Literature on Applied Machine Learning in Metagenomic Classification: A Scoping Review |
title_full_unstemmed |
Literature on Applied Machine Learning in Metagenomic Classification: A Scoping Review |
title_sort |
literature on applied machine learning in metagenomic classification: a scoping review |
publisher |
MDPI AG |
series |
Biology |
issn |
2079-7737 |
publishDate |
2020-12-01 |
description |
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. |
topic |
metagenomics scoping review classification data preprocessing |
url |
https://www.mdpi.com/2079-7737/9/12/453 |
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