Improved time-domain approaches for locating exons in DNA using zero-phase filtering

El-Badawy, Ismail M.; Gasser, Safa M.; Khedr, Mohamed; Aziz, Ashraf M.

doi:10.1109/globalsip.2014.7032340

Cited by 9 publications

(2 citation statements)

References 8 publications

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“…These measurements are commonly used for identification of protein coding regions. [38][39][40]  Specificity (Sp): It measures the proportion of intron regions correctly recognized as intron regions. Conventionally Sp is specified by Eq.24…”

Section: Efiltering For Exon Predictionmentioning

confidence: 99%

A Novel Approach For Identification Of Exon Locations In DNA Sequences Using GLC Window

Kumari

Seventline

2021

International Journal of Biology and Biomedical Engineering

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The application of signal processing techniques for identification of exons in Deoxyribonucleic acid (DNA) sequence is a challenging task. The objective of this paper is to introduce a combinational window approach for locating exons in DNA sequence. In contrast to the traditional single window function for evaluation of short time Fourier transform (STFT), this work proposes a novel method for evaluating STFT coefficients using a combinational window function comprising of Gaussian, Lanczos and Chebyshev (GLC) windows. The chosen combinational window GLC has the highest relative side lobe attenuation values compared to other window functions introduced by various researchers. The proposed algorithm incorporates GLC window function for evaluating STFT coefficients and in the design of FIR bandpass filter. Simulation results revealed its effectiveness in improving the evaluation parameters like Sensitivity, Specificity, Accuracy, Area under curve (AUC), Discrimination Measure (DM). Furthermore, the proposed algorithm has been applied successfully to some universal benchmark datasets like C. elegans, Homosapiens, etc., The proposed method has shown to be an efficient approach for the prediction of protein coding regions compared to other existing methods. All the simulations are done using the MATLAB 2016a.

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Section: Efiltering For Exon Predictionmentioning

confidence: 99%

A Novel Approach For Identification Of Exon Locations In DNA Sequences Using GLC Window

Kumari

Seventline

2021

International Journal of Biology and Biomedical Engineering

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“…Some of these applications are diversity communication systems (El-Ansary et al, 2013;Aziz, 2011a), target detection (Aziz, 2010;El-Ayadi et al, 1996), distributed radar surveillance networks (Aziz, 2014c;Aziz, 2008), wireless sensor networks (Aziz et al, 2011;Aziz, 2011b), biomedical applications (El-Badawy et al, 2014;2013) and target tracking (Aziz, 2013;2011c). An association technique is essential processing in multisensor data fusion systems (Hall, 1992).…”

Section: Introductionmentioning

confidence: 99%

A New Method for Multipath Clustering for Over-the-Horizon Radar

Aziz¹,

Abdelrahman²,

Abouelatta³

2016

American Journal of Applied Sciences

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Over-the-Horizon Radar (OTHR) exploits the refraction of high frequency radiation through the ionosphere layers to detect targets beyond the line-of-sight horizon. Multipath propagation between the radar and the detected targets may results in multiple spatially separated tracks for a single target to be observed at the receiver site. Consequently there is a heavy traffic, especially in case of multiple targets, to be associated and combined if there are tracks represent the same target. In this study, a new method for multipath clustering for OTHR is proposed. The proposed method describes the similarities between all tracks as fuzzy degrees of membership. This method can operate in real-time and can perform clustering and fusion of OTHR tracks with tracks from other sources such as targets reporting global positioning systems and microwave radars. The proposed method has the advantages of less computations and high efficiency compared to conventional fuzzy logic clustering techniques. It has also the advantage of treating all the tracks data at once rather than pairwise. The efficiency of the proposed method is demonstrated using simulated examples.

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Classification of SARS-CoV-2 and non-SARS-CoV-2 using machine learning algorithms

Singh

Vallejo

El-Badawy

et al. 2021

Computers in Biology and Medicine

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Due to the continued evolution of the SARS-CoV-2 pandemic, researchers worldwide are working to mitigate, suppress its spread, and better understand it by deploying digital signal processing ( DSP ) and machine learning approaches. This study presents an alignment-free approach to classify the SARS-CoV-2 using complementary DNA , which is DNA synthesized from the single-stranded RNA virus. Herein, a total of 1582 samples, with different lengths of genome sequences from different regions, were collected from various data sources and divided into a SARS-CoV-2 and a non-SARS-CoV-2 group. We extracted eight biomarkers based on three-base periodicity, using DSP techniques, and ranked those based on a filter-based feature selection. The ranked biomarkers were fed into k-nearest neighbor, support vector machines, decision trees, and random forest classifiers for the classification of SARS-CoV-2 from other coronaviruses. The training dataset was used to test the performance of the classifiers based on accuracy and F-measure via 10-fold cross-validation. Kappa-scores were estimated to check the influence of unbalanced data. Further, 10x10 cross-validation paired t -test was utilized to test the best model with unseen data. Random forest was elected as the best model, differentiating the SARS-CoV-2 coronavirus from other coronaviruses and a control a group with an accuracy of 97.4%, sensitivity of 96.2%, and specificity of 98.2%, when tested with unseen samples. Moreover, the proposed algorithm was computationally efficient, taking only 0.31 seconds to compute the genome biomarkers, outperforming previous studies.

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Improved time-domain approaches for locating exons in DNA using zero-phase filtering

Cited by 9 publications

References 8 publications

A Novel Approach For Identification Of Exon Locations In DNA Sequences Using GLC Window

A Novel Approach For Identification Of Exon Locations In DNA Sequences Using GLC Window

A New Method for Multipath Clustering for Over-the-Horizon Radar

Classification of SARS-CoV-2 and non-SARS-CoV-2 using machine learning algorithms

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