Identification of Pathogenic Viruses Using Genomic Cepstral Coefficients with Radial Basis Function Neural Network

Human populations are constantly inundated with viruses, some of which are responsible for various deadly diseases. Molecular biology approaches have been employed extensively to identify pathogenic viruses despite the limitations of the approaches. Nevertheless, recent advances in the next generation sequencing technologies have led to a surge in viral genome sequence databases with potentials for Bioinformatics based virus identification. In this study, we have utilised the Gaussian radial basis function neural network to identify pathogenic viruses. To validate the neural network model, samples of sequences of four different pathogenic viruses were extracted from the ViPR corpus. Electron-ion interaction pseudopotential scheme was used to encode the extracted sample sequences while cepstral analysis technique was applied to the encoded sequences to obtain a new set of genomic features, here called Genomic Cepstral Coefficients (GCCs). Experiments were performed to determine the potency of the GCCs to discriminate between different pathogenic viruses. Results show that GCCs are highly discriminating and gave good results when applied to identify some selected pathogenic viruse

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

The prevalence of melanoma skin cancer disease is rapidly increasing as recorded death cases of its patients continue to annually escalate. Reliable segmentation of skin lesion is one essential requirement of an efficient noninvasive computer aided diagnosis tool for accelerating the identification process of melanoma. This paper presents a new algorithm based on perceptual color difference saliency along with binary morphological analysis for segmentation of melanoma skin lesion in dermoscopic images. The new algorithm is compared with existing image segmentation algorithms on benchmark dermoscopic images acquired from public corpora. Results of both qualitative and quantitative evaluations of the new algorithm are encouraging as the algorithm performs excellently in comparison with the existing image segmentation algorithms

DeepCOVID-19: A model for identification of COVID-19 virus sequences with genomic signal processing and deep learning

The spread of Coronavirus Disease-2019 worldwide necessitates the development of accurate identification methods and the determination of genetic relatedness. The result of genomic methods involving nucleotide alignment informed the considerations of several alignment-free techniques for virus detection. This paper presents a genomic sequence identification model, developed based on Genomic Signal Processing (GSP), deep learning, and genomic datasets of Coronavirus 2 (SARS-CoV-2), Severe Acute Respiratory Syndrome CoV (SARS-CoV), and Middle East Respiratory Syndrome CoV (MERS-CoV). Our results showed that the Z-Curve images for the three viral strains depicted high visual similarities in texture and color, thus making it difficult to differentiate the strains by visual inspection. However, the homogeneity distance showed that SARS-CoV-2 is closer to SAR-CoV than MERS-CoV. Following a validation accuracy of 98.33%, it became clear that Z-Curve images for MERS-CoV, SARS-CoV and SARS-CoV-2 have distinct features after transformation by the Convolutional Neural Network (CNN) classifier. The divergence in texture and color reflects genetic variation among the strains, which is too insignificant for differentiation via visual inspection. Our results showed that higher layers of CNN amplify aspects of input images that are critical for discrimination, thereby confirming the importance of deep learning and GSP in accurate viral detectio