Paper Info
Title
Feature extraction approaches for biological sequences: a comparative study of mathematical features
Abstract
As consequence of the various genomic sequencing projects, an increasing volume of biological sequence data is being produced. Although machine learning algorithms have been successfully applied to a large number of genomic sequence-related problems, the results are largely affected by the type and number of features extracted. This effect has motivated new algorithms and pipeline proposals, mainly involving feature extraction problems, in which extracting significant discriminatory information from a biological set is challenging. Considering this, our work proposes a new study of feature extraction approaches based on mathematical features (numerical mapping with Fourier, entropy and complex networks). As a case study, we analyze long non-coding RNA sequences. Moreover, we separated this work into three studies. First, we assessed our proposal with the most addressed problem in our review, e.g. lncRNA and mRNA; second, we also validate the mathematical features in different classification problems, to predict the class of lncRNA, e.g. circular RNAs sequences; third, we analyze its robustness in scenarios with imbalanced data. The experimental results demonstrated three main contributions: first, an in-depth study of several mathematical features; second, a new feature extraction pipeline; and third, its high performance and robustness for distinct RNA sequence classification.
Year
DOI
Venue
2021
10.1093/bib/bbab011
BRIEFINGS IN BIOINFORMATICS
Keywords
DocType
Volume
feature extraction, biological sequences, numerical mapping, Fourier, entropy, complex networks
Journal
22
Issue
ISSN
Citations 
5
1467-5463
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Robson P Bonidia100.34
Lucas D H Sampaio200.34
Douglas Silva Domingues332.43
Alexandre R Paschoal400.34
Fabrício Martins Lopes51129.14
André C P L F de Carvalho601.35
Danilo S Sanches700.34