VIRMOTIF: A User-Friendly Tool for Viral Sequence Analysis

Rajaei, Pedram; Jahanian, Khadijeh; Beheshti, Amin; Band, Shahab S.; Dehzangi, Abdollah; Alinejad‐Rokny, Hamid

doi:10.3390/genes12020186

Cited by 23 publications

(14 citation statements)

References 29 publications

(33 reference statements)

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“…We quantified the representation of short sequence motifs (di-, tri- and tetra-nucleotides) in all viral genomes using our previously reported Markov-based representation (D-value) method 25 – 27 . Briefly, motif representation (D-value) is defined as the ratio of the observed frequency ( P obs ) of a motif over its expected frequency ( P exp ).…”

Section: Methodsmentioning

confidence: 99%

The low abundance of CpG in the SARS-CoV-2 genome is not an evolutionarily signature of ZAP

Afrasiabi

Alinejad‐Rokny

Khosh

et al. 2022

Sci Rep

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The zinc finger antiviral protein (ZAP) is known to restrict viral replication by binding to the CpG rich regions of viral RNA, and subsequently inducing viral RNA degradation. This enzyme has recently been shown to be capable of restricting SARS-CoV-2. These data have led to the hypothesis that the low abundance of CpG in the SARS-CoV-2 genome is due to an evolutionary pressure exerted by the host ZAP. To investigate this hypothesis, we performed a detailed analysis of many coronavirus sequences and ZAP RNA binding preference data. Our analyses showed neither evidence for an evolutionary pressure acting specifically on CpG dinucleotides, nor a link between the activity of ZAP and the low CpG abundance of the SARS-CoV-2 genome.

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Section: Methodsmentioning

confidence: 99%

The low abundance of CpG in the SARS-CoV-2 genome is not an evolutionarily signature of ZAP

Afrasiabi

Alinejad‐Rokny

Khosh

et al. 2022

Sci Rep

Self Cite

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“…The medical and biological datasets are increasing rapidly. To analyse such big and complex data, artificial intelligence and machine learning algorithms become most popular [10][11][12][13][14][15][16]. Therefore, it is important to implement novel techniques to uncover the medical and biological patterns.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Feature Fusion and Machine Learning Approaches for Melanoma Skin Cancer Detection

Rahman¹,

Nasir²,

A-Alam³

et al. 2022

Preprint

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Skin cancer is an exquisite disease globally nowadays. Because of the poor contrast and apparent resemblance between skin and lesions, automatic identification of skin cancer is complicated. The rate of human death can be massively reduced if melanoma skin cancer can be detected quickly using dermoscopy images. In this research, an anisotropic diffusion filtering method is used on dermoscopy images to remove multiplicative speckle noise and the fast-bounding box (FBB) method is applied to segment the skin cancer region. Furthermore, the paper consists of two feature extractor parts. One of the two features extractor parts is the hybrid feature extractor (HFE) part and another is the convolutional neural network VGG19 based CNN feature extractor part. The HFE portion combines three feature extraction approaches into a single fused feature vector: Histogram-Oriented Gradient (HOG), Local Binary Pattern (LBP), and Speed Up Robust Feature (SURF). The CNN method also is used to extract additional features from test and training datasets. This two-feature vector is fused to design the classification model. This classifier performs the classification of dermoscopy images whether it is melanoma or non-melanoma skin cancer. The proposed methodology is performed on two ordinary datasets and achieved the accuracy 99.85%, sensitivity 91.65%, and specificity 95.70%, which makes it more successful than previous machine learning algorithms.

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“…The medical and biological datasets are increasing rapidly. To analyse such big and complex data, artificial intelligence and machine learning algorithms become most popular [26][27][28][29][30][31][32][33][34][35]. Therefore, it is important to implement novel techniques to uncover the medical and biological patterns.…”

Section: Introductionmentioning

confidence: 99%

Hybrid HCNN-KNN Transfer Learning Model Enhances Age Estimation Accuracy in Orthopantomography

Sharifonnasabi¹,

Jhanjhi²,

Jacob³

et al. 2022

Preprint

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Age estimation in dental radiographs Orthopantomography (OPG) is a medical imaging technique that physicians and pathologists utilise for disease identification and legal matters. For example, for estimating post-mortem interval, detecting child abuse, drug trafficking, and identifying an unknown body. Recent development in automated image processing models improved the age estimation's limited precision to an approximate range of +/- one year. While this estimation is often accepted as accurate measurement, age estimation should be as precise as possible in most serious matters, such as homicide. Current age estimation techniques are highly dependent on manual and time-consuming image processing. Age estimation is often a time-sensitive matter in which the image processing time is vital. Recent development in Machine learning-based data processing methods has decreased the imaging time processing; however, the accuracy of these techniques remains to be further improved. We proposed an ensemble method of image classifiers and transfer learning techniques to enhance the accuracy of age estimation using OPGs from one year to a couple of months (1-3-6). This hybrid model is based on convolutional neural networks (CNN) and K nearest neighbours (KNN). The hybrid (HCNN-KNN) model was used to investigate 1,922 panoramic dental radiographs of patients aged 15 to 23. These OPGs were obtained from the various teaching institutes and private dental clinics in Malaysia. To minimise the chance of overfitting in our model, we used the principal component analysis (PCA) algorithm and eliminated the features with high correlation. To further enhance the performance of our hybrid model, we performed systematic image pre-processing. We applied a series of classifications to train our model. We have successfully demonstrated that combining these innovative approaches has improved the classification and segmentation and thus the age-estimation outcome of the model. Our findings suggest that our innovative model, for the first time, to the best of our knowledge, successfully estimated the age in classified studies of one year old, six months, three months and one-month-old cases with accuracies of 99.98, 99.96, 99.87, and 98.78 respectively.

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VIRMOTIF: A User-Friendly Tool for Viral Sequence Analysis

Cited by 23 publications

References 29 publications

The low abundance of CpG in the SARS-CoV-2 genome is not an evolutionarily signature of ZAP

The low abundance of CpG in the SARS-CoV-2 genome is not an evolutionarily signature of ZAP

Hybrid Feature Fusion and Machine Learning Approaches for Melanoma Skin Cancer Detection

Hybrid HCNN-KNN Transfer Learning Model Enhances Age Estimation Accuracy in Orthopantomography

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