The Helitron family classification using SVM based on Fourier transform features applied on an unbalanced dataset

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

doi:10.1007/s11517-019-02027-5

Cited by 7 publications

(9 citation statements)

References 48 publications

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“…The numerical genomic representation using coding methods is an important step to visualize and characterize the hidden information that can be contain in it, especially in this case where the nucleotide sequences do not have any continuously or homologous between them. Different coding techniques exist: the binary [20], the structural bending trinucleotide (PNUC) [21], the electron-ion interaction pseudopotential (EIIP) mapping [22], the FCGS [23][24][25], and so on. In addition, several signal processing techniques were applied with success to detect the relationship between sequences and detect some biological repetitive sequences, and so on.…”

Section: Time-frequency Analysis Techniquementioning

confidence: 99%

“…Then, 1-D signals are generated by applying the FCGS order 2: FCGS 2 . This type of coding technique is based on the apparition's probability of two successive nucleotides in an entry sequence [23][24][25]. The probability (P 2_nuc ) of given L nucleotides in the sequence is as follows:…”

Section: Genomic Signal Representationmentioning

confidence: 99%

“…The sequence size and the type of window influence the frequency parameters values. N and R couple are taken as a power of two, this is recommended by the Fast Fourier Transform (FFT) algorithm [20][21][22][23].…”

Section: Smoothed Discrete Fourier Transform: Sdftmentioning

confidence: 99%

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Comparative genomic signature representations of the emerging COVID-19 coronavirus and other coronaviruses: High identity and possible recombination between Bat and Pangolin coronaviruses

et al. 2020

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Coronaviruses are responsible on respiratory diseases in animal and human. The combination of numerical encoding techniques and digital signal processing methods are becoming increasingly important in handling large genomic data. In this paper, we propose to analyze the SARS-CoV-2 genomic signature using the combination of different nucleotide representations and signal processing tools in the aim to identify its genetic origin. The sequence of SARS-CoV-2 was compared with 21 relevant sequences including Bat, Yak and Pangolin coronavirus sequences. In addition, we developed a new algorithm to locate the nucleotide modifications. The results show that the Bat and Pangolin coronaviruses were the most related to SARS-CoV-2 with 96% and 86% of identity all along the genome. Within the S gene sequence, the Pangolin sequence presents local highest nucleotide identity. Those findings suggest genesis of SARS-Cov-2 through evolution from Bat and Pangolin strains. This study offers new ways to automatically characterize viruses.

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Section: Time-frequency Analysis Techniquementioning

confidence: 99%

Section: Genomic Signal Representationmentioning

confidence: 99%

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Comparative genomic signature representations of the emerging COVID-19 coronavirus and other coronaviruses: High identity and possible recombination between Bat and Pangolin coronaviruses

et al. 2020

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“…This transformation is called “DNA coding”. In this work, we opted for a special coding technique called “Order 2 Frequency Chaos Game Signal” (FCGS 2 ) [ 23 , 24 ]. The FCGS 2 coding is a statistical representation of DNA.…”

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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“…In formula (15), the sample points that can make the equation hold are support vectors. From a geometric point of view, the support vector is the sample points on the two classification boundaries w T x i + b � 1 and w…”

Section: Construction Of Financial Risk Prediction Modelmentioning

confidence: 99%

Bank Financial Risk Prediction Model Based on Big Data

Peng

Lin²,

Wu³

2022

Scientific Programming

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Financial risk prediction is an important technique to systematically predict the unforeseeable risks in banking systems. The issues involving ill-timing and low accuracy in the current risks prediction methods necessitate an effective risk prediction method. Akin to the use of big data in various domains, the technology has a significant role in financial services and can be used to accurately and timely predict the possibilities of risks. In this paper, an effective hybrid method is proposed to aptly and effectively predict financial risks in the banking systems. The method utilizes the Lasso and linear regression algorithms via the big data features and framework technologies. By proper formalization of the bank financial risk problems, the risk data is obtained and processed. To filter the initial text features and preprocess the annual report text data, the information gain method is used. With the Bag-of-Words (BoW) and the word frequency reverse document frequency weighting method, the text features of financial risk prediction are extracted. The bank financial risk prediction model is constructed based on weighted fusion adaptive random subspace algorithm. The prediction results obtained are integrated so as to realize the bank financial risks in a seamless way. The experimental results show that the proposed method can effectively improve the prediction accuracy and consumes comparatively lesser time in risk prediction.

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The Helitron family classification using SVM based on Fourier transform features applied on an unbalanced dataset

Cited by 7 publications

References 48 publications

Comparative genomic signature representations of the emerging COVID-19 coronavirus and other coronaviruses: High identity and possible recombination between Bat and Pangolin coronaviruses

Comparative genomic signature representations of the emerging COVID-19 coronavirus and other coronaviruses: High identity and possible recombination between Bat and Pangolin coronaviruses

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

Bank Financial Risk Prediction Model Based on Big Data

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