Feature Extraction of Long Non-coding RNAs: A Fourier and Numerical Mapping Approach

Bonidia, Robson Parmezan; Sampaio, Lucas Dias Hiera; Lopes, Fabrício Martins; Sanches, Danilo Sipoli

doi:10.1007/978-3-030-33904-3_44

Cited by 8 publications

(14 citation statements)

References 28 publications

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“…As preprocessing, we used only sequences longer than 200nt [57], and we also removed sequence redundancy. Moreover, the sampling method was adopted in our dataset, since we are faced with the imbalanced data problem [20]. Therefore, we applied random majority undersampling, which consists of removing samples from the majority class (to adjust the class distribution) [74].…”

Section: Data Selectionmentioning

confidence: 99%

“…This method is has been widely studied in bioinformatics, mainly for analysis of periodicities and repetitive elements in DNA sequences [81] and protein structures [82]. This approach is shown in Figure 3 and was based on [20]. To calculate DFT, we will use the Fast Fourier Transform (FFT), that is a highly efficient procedure for computing the DFT of a time series [83].…”

Section: Fourier Transform and Numerical Mappingsmentioning

confidence: 99%

“…Consequently, the application of a normalization procedure makes the ranges similar, reducing this problem [106]. We used the min-max normalization, which reduces the data range to 0 and 1 (or -1 to 1, if there are negative values) [20]. The general formula is given as (Equation ( 24)) [107]:…”

Section: Normalization Training and Evaluation Metricsmentioning

confidence: 99%

“…Thereby, modern applications extract relevant features from sequences based on several biological properties, e.g., physicochemical, Open Reading Frames (ORF)-based, usage frequency of adjoining nucleotide triplets, GC content, among others. This approach is common in biological problems, but these implementations are often difficult to reuse or adapt to another specific problem, e.g., ORF features are an essential guideline for distinguishing Long non-coding RNAs (lncRNA) from protein-coding genes [19], but not useful features for classifying lncRNA classes [20,21] (e.g., in [21], ORF score (feature importance) is less than 0.009 to classify circular RNA from other types of lncRNAs). Consequently, the feature extraction problem arises, in which extracting a set of useful features that contain significant discriminatory information becomes a fundamental step in the construction of a predictive model [22].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Feature extraction approaches for biological sequences: a comparative study of mathematical features

Bonidia

Sampaio

Domingues

et al. 2021

Briefings in Bioinformatics

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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

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Section: Data Selectionmentioning

confidence: 99%

Section: Fourier Transform and Numerical Mappingsmentioning

confidence: 99%

Section: Normalization Training and Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Feature extraction approaches for biological sequences: a comparative study of mathematical features

Bonidia

Sampaio

Domingues

et al. 2021

Briefings in Bioinformatics

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“…The EIIP values of the nucleotides were proposed by Nair and Sreenadhan 25 , and have been utilized to address various problems in the field of bioinformatics 26,27 . In EIIP, each nucleotide of an mRNA sequence is encoded by a numerical value corresponding to the distribution of free electron energies.…”

Section: Eiip+pseeiipmentioning

confidence: 99%

XG-ac4C: identification of N4-acetylcytidine (ac4C) in mRNA using eXtreme gradient boosting with electron-ion interaction pseudopotentials

Alam

Tayara

Chong

2020

Sci Rep

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N4-acetylcytidine (ac4C) is a post-transcriptional modification in mRNA which plays a major role in the stability and regulation of mRNA translation. The working mechanism of ac4C modification in mRNA is still unclear and traditional laboratory experiments are time-consuming and expensive. Therefore, we propose an XG-ac4C machine learning model based on the eXtreme Gradient Boost classifier for the identification of ac4C sites. The XG-ac4C model uses a combination of electron-ion interaction pseudopotentials and electron-ion interaction pseudopotentials of trinucleotide of the nucleotides in ac4C sites. Moreover, Shapley additive explanations and local interpretable model-agnostic explanations are applied to understand the importance of features and their contribution to the final prediction outcome. The obtained results demonstrate that XG-ac4C outperforms existing state-of-the-art methods. In more detail, the proposed model improves the area under the precision-recall curve by 9.4% and 9.6% in cross-validation and independent tests, respectively. Finally, a user-friendly web server based on the proposed model for ac4C site identification is made freely available at http://nsclbio.jbnu.ac.kr/tools/xgac4c/.

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Plant Disease Detection and Classification Using Machine Learning and Deep Learning Techniques: Current Trends and Challenges

Alsakar,

Sakr,

Elmogy

2023

Lecture Notes in Networks and Systems

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Feature Extraction of Long Non-coding RNAs: A Fourier and Numerical Mapping Approach

Cited by 8 publications

References 28 publications

Feature extraction approaches for biological sequences: a comparative study of mathematical features

Feature extraction approaches for biological sequences: a comparative study of mathematical features

XG-ac4C: identification of N4-acetylcytidine (ac4C) in mRNA using eXtreme gradient boosting with electron-ion interaction pseudopotentials

Plant Disease Detection and Classification Using Machine Learning and Deep Learning Techniques: Current Trends and Challenges

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