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. Availability: https://github.com/Bonidia/FeatureExtraction_BiologicalSequences

Predicting Parkinson’s Disease Related Genes Based on PyFeat and Gradient Boosted Decision Tree (GBDT)

“…First, the preprocessing step for removing duplication of genes is followed by representing genes as DNA FASTA sequences and removing redundancies of sequences. Second, from the DNA sequences, the most significant features are extracted using different feature extraction 24 2020 Diagnosing between diferent cases lncRNAs DFT, Entropy, RefSeq, GreeNC Complex Network Ensembl (v87, v32) Bi et al 23 2021 Predicting PD-related genes brain regions CERNNE PPMI ACC = 88.57%…”

“…The proposed system represents all genes as DNA FASTA sequences to get all essential and distinguishing information. We extract the most significant features of these FASTA sequences using five numerical Fourier transform 19,24 and PyFeat method with AB as a feature selection technique 15 . The selected features are fed to the GBDT technique to aid in the diagnosis of different test cases.…”

“…It is commonly utilized in digital image and signal processing fields. DFT can reveal hidden periodicities after translating from the time domain to frequency domain 24 . The DFT for a signal at frequency f , with length…”

“…Features: For each representation, we employ the feature extraction depending on the peak to average power ratio (PAPR), signal to noise ratio (SNR), minimum, maximum, median, population standard deviation, sample standard deviation, percentile (15/25/50/75), variance, coefficient of variation, amplitude, semi-interquartile range, interquartile range, skewness, and kurto-sis 19,24 .…”

“…for DNA sequence is defined by equation (24), where kGap = 5, then 5120 features are existed, these features are determined by the frequencies of sub-sequences: AA -AAA, . .…”