MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors

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dc.contributor.authorBonidia, Robson P.
dc.contributor.authorDomingues, Douglas S. [UNESP]
dc.contributor.authorSanches, Danilo S.
dc.contributor.authorde Carvalho, André C P L F
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUTFPR
dc.date.accessioned2022-04-28T19:50:15Z
dc.date.available2022-04-28T19:50:15Z
dc.date.issued2022-01-17
dc.description.abstractOne of the main challenges in applying machine learning algorithms to biological sequence data is how to numerically represent a sequence in a numeric input vector. Feature extraction techniques capable of extracting numerical information from biological sequences have been reported in the literature. However, many of these techniques are not available in existing packages, such as mathematical descriptors. This paper presents a new package, MathFeature, which implements mathematical descriptors able to extract relevant numerical information from biological sequences, i.e. DNA, RNA and proteins (prediction of structural features along the primary sequence of amino acids). MathFeature makes available 20 numerical feature extraction descriptors based on approaches found in the literature, e.g. multiple numeric mappings, genomic signal processing, chaos game theory, entropy and complex networks. MathFeature also allows the extraction of alternative features, complementing the existing packages. To ensure that our descriptors are robust and to assess their relevance, experimental results are presented in nine case studies. According to these results, the features extracted by MathFeature showed high performance (0.6350-0.9897, accuracy), both applying only mathematical descriptors, but also hybridization with well-known descriptors in the literature. Finally, through MathFeature, we overcame several studies in eight benchmark datasets, exemplifying the robustness and viability of the proposed package. MathFeature has advanced in the area by bringing descriptors not available in other packages, as well as allowing non-experts to use feature extraction techniques.en
dc.description.affiliationInstitute of Mathematics and Computer Sciences University of São Paulo
dc.description.affiliationGroup of Genomics and Transcriptomes in Plants Institute of Biosciences São Paulo State University (UNESP)
dc.description.affiliationDepartment of Computer Science Federal University of Technology - Paraná UTFPR
dc.description.affiliationUnespGroup of Genomics and Transcriptomes in Plants Institute of Biosciences São Paulo State University (UNESP)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipIdFAPESP: 2013/07375-0
dc.identifierhttp://dx.doi.org/10.1093/bib/bbab434
dc.identifier.citationBriefings in bioinformatics, v. 23, n. 1, 2022.
dc.identifier.doi10.1093/bib/bbab434
dc.identifier.issn1477-4054
dc.identifier.scopus2-s2.0-85123814372
dc.identifier.urihttp://hdl.handle.net/11449/223373
dc.language.isoeng
dc.relation.ispartofBriefings in bioinformatics
dc.sourceScopus
dc.subjectbiological sequences
dc.subjectfeature extraction
dc.subjectGUI-based platform
dc.subjectmathematical descriptors
dc.subjectpackage
dc.subjectpython
dc.titleMathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptorsen
dc.typeArtigo

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