A combined support vector machine-FCGS classification based on the wavelet transform for Helitrons recognition in C.elegans

Touati, Rabeb; Messaoudi, Imen; Oueslati, Afef Elloumi; Lachiri, Zied

doi:10.1007/s11042-018-6455-x

Cited by 11 publications

(12 citation statements)

References 45 publications

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“…SVMs were applied to the classification process of TEs, such as in TEClass [168], and recently in the identification of Helitrons (an order of Class II transposons) [216], showing high precision rates. On the other hand, the TE-Learner framework uses a random forest to classify LTR retrotransposons, but the identification is done using traditional bioinformatics approaches [26].…”

Section: How Can Machine Learning and Deep Learning Techniques Impmentioning

confidence: 99%

Retrotransposons in Plant Genomes: Structure, Identification, and Classification through Bioinformatics and Machine Learning

Orozco-Arias

Isaza

Guyot

2019

IJMS

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Transposable elements (TEs) are genomic units able to move within the genome of virtually all organisms. Due to their natural repetitive numbers and their high structural diversity, the identification and classification of TEs remain a challenge in sequenced genomes. Although TEs were initially regarded as “junk DNA”, it has been demonstrated that they play key roles in chromosome structures, gene expression, and regulation, as well as adaptation and evolution. A highly reliable annotation of these elements is, therefore, crucial to better understand genome functions and their evolution. To date, much bioinformatics software has been developed to address TE detection and classification processes, but many problematic aspects remain, such as the reliability, precision, and speed of the analyses. Machine learning and deep learning are algorithms that can make automatic predictions and decisions in a wide variety of scientific applications. They have been tested in bioinformatics and, more specifically for TEs, classification with encouraging results. In this review, we will discuss important aspects of TEs, such as their structure, importance in the evolution and architecture of the host, and their current classifications and nomenclatures. We will also address current methods and their limitations in identifying and classifying TEs.

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Section: How Can Machine Learning and Deep Learning Techniques Impmentioning

confidence: 99%

Retrotransposons in Plant Genomes: Structure, Identification, and Classification through Bioinformatics and Machine Learning

Orozco-Arias

Isaza

Guyot

2019

IJMS

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“…The conversion of the genomic sequences into numerical ones using signal processing tools is an important step to characterize the unknown region [ 25 – 28 ]. It allows rapid image observation of similar patterns before assessing more precise analysis.…”

Section: Methodsmentioning

confidence: 99%

“…After transforming the amino-acid sequence into a signal, we aimed to see it in a 2-D representation to focus more on the information that can contain. The Continuous Wavelet Transform (CWT) (along 64 scales with w 0 ~ 5.5) was applied to protein signal to obtain a protein image (2-D scalogram) [ 27 , 28 ]. corresponds to the oscillations number of wavelet transform, and the parameter f 0 is the central frequency of the basic wavelet.…”

Section: Methodsmentioning

confidence: 99%

Intelligent system based comparative analysis study of SARS-CoV-2 spike protein and antigenic proteins in different types of vaccines

Touati

Elngar

2022

Beni-Suef Univ J Basic Appl Sci

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Background Coronaviruses, members of the Coronavirinae subfamily in the Coronaviridae family, are enveloped and positive-stranded RNA viruses that infect animals and humans, causing intestinal and respiratory infections. Coronavirus disease 2019 (COVID-19) is caused by a novel coronavirus, named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). This disease appeared, for the first time (December 2019), in China and has spread quickly worldwide causing a large number of deaths. Considering the global threat, the World Health Organization (WHO) has declared, in March 2020, COVID-19 as a pandemic. Many studies suggest the great effect of the existing vaccines to protect against symptomatic cases of death by the COVID-19 virus. This paper, proposes to compare the main antigenic proteins sequences of the existing vaccines with Spike (S) protein of the SARS-CoV-2 genome. Our choice of S protein is justified by the major role that plays it in the receptor recognition and membrane fusion process based on an intelligent system. Herein, we focus on finding a correlation between S protein and compulsory vaccines in the countries that have a less death number by COVID-19 virus. In this work, we have used a combination of coding methods, signal processing, and bioinformatic techniques with the goal to localize the similar patterns between the S gene of the SARS-Cov-2 genome and 14 investigated vaccines. Results A total of 8 similar sequences which have a size more than 6 amino acids were identified. Further, these comparisons propose that these segments can be implicated in the immune response against COVID-19, which may explain the wide variation by country in the severity of this viral threat. Conclusions Our in silico study suggests a possible protective effect of Poliovirus, HIB, Hepatitis B, PCV10, Measles, Mumps, and Rubella (MMR) vaccines against COVID-19.

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“…Indeed even if the repetition pattern contains variations in nucleotide composition, this does not greatly impact the overall shape of the repetitive pattern at the level of DNA image. Furthermore, our choice for this method is reinforced by its performance in characterizing different classes of transposable elements [ 30 , 31 ]. For the wavelet analysis, we use the complex Morlet wavelet which is best suited to localize repetitive DNA in the time-frequency domain.…”

Section: Methodsmentioning

confidence: 99%

New methodology for repetitive sequences identification in human X and Y chromosomes

Touati

Tajouri

Mesaoudi

et al. 2021

Biomedical Signal Processing and Control

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A combined support vector machine-FCGS classification based on the wavelet transform for Helitrons recognition in C.elegans

Cited by 11 publications

References 45 publications

Retrotransposons in Plant Genomes: Structure, Identification, and Classification through Bioinformatics and Machine Learning

Retrotransposons in Plant Genomes: Structure, Identification, and Classification through Bioinformatics and Machine Learning

Intelligent system based comparative analysis study of SARS-CoV-2 spike protein and antigenic proteins in different types of vaccines

New methodology for repetitive sequences identification in human X and Y chromosomes

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