Graphical Representation of Biological Sequences

Mizuta, Satoshi

doi:10.5772/intechopen.74795

Cited by 6 publications

(4 citation statements)

References 57 publications

(61 reference statements)

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“…Nowadays, genomic image processing (GIP) techniques [17,18] start to be used to detect different genetic diseases to avoid the drawbacks of the previously described diagnostic methods. GIP is an engineering field that has been defined as the processing of the images extracted from the genetic sequences using different mapping techniques to obtain biological knowledge and the translation of that knowledge into systems that have great consideration in the detection of the various genetic diseases [17][18][19][20]. Our approach mainly depends on employing a GIP technique with machine learning models.…”

Section: Introductionmentioning

confidence: 99%

Detection of COVID-19 Using Genomic Image Processing Techniques

Hammad

Ghoneim

Mabrouk

2021

2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)

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Novel Coronavirus Disease 2019 (COVID-19) is a new pandemic that appeared at the end of March 2019 in Wuhan city, China, which affected millions worldwide. COVID-19 is caused by the novel severe acute respiratory syndrome coronavirus 2 (SARSCoV-2) epidemic. Also, several viral epidemics have been listed in the last two decades, like the middle east respiratory syndrome coronavirus (MERSCoV) and the severe acute respiratory syndrome coronavirus 1 (SARSCoV-1), which cause MERS, and SARS diseases, respectively. Detection of these viral epidemics is a difficult issue because of their genetic similarity. In this paper, an effective automated system was developed to classify these viral epidemics using their complete genomic sequences via the genomic image processing techniques to facilitate the diagnosis and increase the detection accuracy in a short time. Results achieved an overall accuracy of 100% using two classifiers: SVM and KNN. However, the KNN classifier shows a privilege over the SVM in the execution time performance.

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Section: Introductionmentioning

confidence: 99%

Detection of COVID-19 Using Genomic Image Processing Techniques

Hammad

Ghoneim

Mabrouk

2021

2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES)

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“…The first graphical representation methods were based on walks in three [18,19] and two [20][21][22] dimensions. Since then, there has been a dynamic development of the graphical bioinformatics branch observed (for reviews see [23,24]). Let us just mention the last few methods of graphical representation: in the "Spider representation of DNA sequences", the graphs resemble a spider's web [25]; in a method called by us "Spectraldynamic representation of DNA sequences", the plots resemble atomic, molecular, or stellar spectra composed of sequences of sharp spectral lines [11].…”

Section: G G T T G G a A 2 G T G T G A G Amentioning

confidence: 99%

4D-Dynamic Representation of DNA/RNA Sequences: Studies on Genetic Diversity of Echinococcus multilocularis in Red Foxes in Poland

Bielińska-Wąż

Wąż

Lass

et al. 2022

Life

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The 4D-Dynamic Representation of DNA/RNA Sequences, an alignment-free bioinformatics method recently developed by us, has been used to study the genetic diversity of Echinococcus multilocularis in red foxes in Poland. Sequences of three mitochondrial genes, i.e., NADH dehydrogenase subunit 2 (nad2), cytochrome b (cob), and cytochrome c oxidase subunit 1 (cox1), are analyzed. The sequences are represented by sets of material points in a 4D space, i.e., 4D-dynamic graphs. As a visualization of the sequences, projections of the graphs into 3D space are shown. The differences between 3D graphs corresponding to European, Asian, and American haplotypes are small. Numerical characteristics (sequence descriptors) applied in the studies can recognize the differences. The concept of creating descriptors of 4D-dynamic graphs has been borrowed from classical dynamics; these are coordinates of the centers or mass and moments of inertia of 4D-dynamic graphs. Based on these descriptors, classification maps are constructed. The concentrations of points in the maps indicate one Polish haplotype (EmPL9) of Asian origin.

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“…Bio data encoded by either of above schemes can be sequentially processed through mathematical operations (such as Fourier transform and wavelet transformation [ 21 ]) or goes through parallel image processing [ 22 ] by optical processors [ 12 ]. Considering the latter approach, various graphical encoding methods have been developed, each targeting one of three above categories as a means of optical image processing.…”

Section: Related Workmentioning

confidence: 99%

Optical pattern generator for efficient bio-data encoding in a photonic sequence comparison architecture

2021

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In this study, optical technology is considered as SA issues' solution with the potential ability to increase the speed, overcome memory-limitation, reduce power consumption, and increase output accuracy. So we examine the effect of bio-data encoding and the creation of input images on the pattern-recognition error-rate at the output of optical Vander-lugt correlator. Moreover, we present a genetic algorithm-based coding approach, named as GAC, to minimize output noises of cross-correlating data. As a case study, we adopt the proposed coding approach within a correlation-based optical architecture for counting k-mers in a DNA string. As verified by the simulations on Salmonella whole-genome, we can improve sensitivity and speed more than 86% and 81%, respectively, compared to BLAST by using coding set generated by GAC method fed to the proposed optical correlator system. Moreover, we present a comprehensive report on the impact of 1D and 2D cross-correlation approaches, as-well-as various coding parameters on the output noise, which motivate the system designers to customize the coding sets within the optical setup.

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Graphical Representation of Biological Sequences

Cited by 6 publications

References 57 publications

Detection of COVID-19 Using Genomic Image Processing Techniques

Detection of COVID-19 Using Genomic Image Processing Techniques

4D-Dynamic Representation of DNA/RNA Sequences: Studies on Genetic Diversity of Echinococcus multilocularis in Red Foxes in Poland

Optical pattern generator for efficient bio-data encoding in a photonic sequence comparison architecture

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