@article{fdi:010077475,
  title = {{A} systematic review of the application of machine learning in the detection and classification of transposable elements},
  author = {{O}rozco-{A}rias, {S}. and {I}saza, {G}. and {G}uyot, {R}omain and {T}abares-{S}oto, {R}.},
  editor = {},
  language = {{ENG}},
  abstract = {{B}ackground: {T}ransposable elements ({TE}s) constitute the most common repeated sequences in eukaryotic genomes. {R}ecent studies demonstrated their deep impact on species diversity, adaptation to the environment and diseases. {A}lthough there are many conventional bioinformatics algorithms for detecting and classifying {TE}s, none have achieved reliable results on different types of {TE}s. {M}achine learning ({ML}) techniques can automatically extract hidden patterns and novel information from labeled or non-labeled data and have been applied to solving several scientific problems. {M}ethodology: {W}e followed the {S}ystematic {L}iterature {R}eview ({SLR}) process, applying the six stages of the review protocol from it, but added a previous stage, which aims to detect the need for a review. {T}hen search equations were formulated and executed in several literature databases. {R}elevant publications were scanned and used to extract evidence to answer research questions. {R}esults: {S}everal {ML} approaches have already been tested on other bioinformatics problems with promising results, yet there are few algorithms and architectures available in literature focused specifically on {TE}s, despite representing the majority of the nuclear {DNA} of many organisms. {O}nly 35 articles were found and categorized as relevant in {TE} or related fields. {C}onclusions: {ML} is a powerful tool that can be used to address many problems. {A}lthough {ML} techniques have been used widely in other biological tasks, their utilization in {TE} analyses is still limited. {F}ollowing the {SLR}, it was possible to notice that the use of {ML} for {TE} analyses (detection and classification) is an open problem, and this new field of research is growing in interest.},
  keywords = {{T}ransposable elements ; {R}etrotransposons ; {D}etection ; {C}lassification ; {B}ioinformatics ; {M}achine learning ; {D}eep learning},
  booktitle = {},
  journal = {{P}eer{J}},
  volume = {7},
  numero = {},
  pages = {e8311 [29 p.]},
  ISSN = {2167-8359},
  year = {2019},
  DOI = {10.7717/peerj.8311},
  URL = {https://www.documentation.ird.fr/hor/fdi:010077475},
}