Denudation of COVID-19 Genome

The Covid-19 virus is a Sars virus that has killed millions of people around the world. It's crucial to know the genotype of the covid virus in order to understand its structure and find vaccines. A genome can be defined as the complete DNA present in the organism which contains the complete set of genetic instruction. In living creatures, the genome is contained in long DNA molecules called chromosomes. A comparative study has been done by using the datasets containing Sars genome, Mers genome, Civets with Sars like genome, bats with Sars like genome, Dengue virus genome, camel with CoV genome, Tuberculosis genome and hedgehog with covid like genome along with the covid genome and draw conclusions regarding the similarities and differences in the nucleotide sequences of Covid-19 genome. © 2022 IEEE.

Recent Dimensionality Reduction Techniques for High-Dimensional COVID-19 Data

We are going through the last years of the COVID-19 pandemic, where almost the entire research community has focused on the challenges that constantly arise. From the computational and mathematical perspective, we have to deal with a dataset with ultra-high volume and ultra-high dimensionality in several experimental studies. An indicative example is DNA sequencing technologies, which offer a more realistic picture of human diseases at the molecular biology level. However, these technologies produce data with high complexity and ultra-high dimensionality. On the other hand, dimensionality reduction techniques are the first choice to address this complexity, revealing the hidden data structure in the original multidimensional space. Also, such techniques can improve the efficiency of machine learning tasks such as classification and clustering. Towards this direction, we study the behavior of seven well-known and cutting-edge dimensionality reduction techniques tailored for RNA-sequencing data. Along with the study of the effect of these algorithms, we propose the extension of the Random projection and Geodesic distance t-Stochastic Neighbor Embedding (RGt-SNE) algorithm, a recent t-Stochastic Neighbor Embedding (t-SNE) improvement. We suggest a new distance criterion for the kernel matrix construction. Our results show the potential of the proposed algorithm and, at the same time, highlight the complexity of the COVID-19 data, which are not separable, creating a significant challenge that the Machine Learning field will have to face. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Deep Learning Approach for Viral DNA Sequence Classification using Genetic Algorithm

DNA sequence classification is one of the major challenges in biological data processing. The identification and classification of novel viral genome sequences drastically help in reducing the dangers of a viral outbreak like COVID-19. The more accurate the classification of these viruses, the faster a vaccine can be produced to counter them. Thus, more accurate methods should be utilized to classify the viral DNA. This research proposes a hybrid deep learning model for efficient viral DNA sequence classification. A genetic algorithm (GA) was utilized for weight optimization with Convolutional Neural Networks (CNN) architecture. Furthermore, Long Short-Term Memory (LSTM) as well as Bidirectional CNN-LSTM model architectures are employed. Encoding methods are needed to transform the DNA into numeric format for the proposed model. Three different encoding methods to represent DNA sequences as input to the proposed model were experimented: k-mer, label encoding, and one hot vector encoding. Furthermore, an efficient oversampling method was applied to overcome the imbalanced dataset issues. The performance of the proposed GA optimized CNN hybrid model using label encoding achieved the highest classification accuracy of 94.88% compared with other encoding methods © 2022, International Journal of Advanced Computer Science and Applications.All Rights Reserved.