Comparing Machine Learning Algorithms with or without Feature Extraction for DNA Classification

The classification of DNA sequences is a key research area in bioinformatics as it enables researchers to conduct genomic analysis and detect possible diseases. In this paper, three state-of-the-art algorithms, namely Convolutional Neural Networks, Deep Neural Networks, and N-gram Probabilistic Models, are used for the task of DNA classification. Furthermore, we introduce a novel feature extraction method based on the Levenshtein distance and randomly generated DNA sub-sequences to compute information-rich features from the DNA sequences. We also use an existing feature extraction method based on 3-grams to represent amino acids and combine both feature extraction methods with a multitude of machine learning algorithms. Four different data sets, each concerning viral diseases such as Covid-19, AIDS, Influenza, and Hepatitis C, are used for evaluating the different approaches. The results of the experiments show that all methods obtain high accuracies on the different DNA datasets. Furthermore, the domain-specific 3-gram feature extraction method leads in general to the best results in the experiments, while the newly proposed technique outperforms all other methods on the smallest Covid-19 datasetComment: 17 page

PLoS One

The evolutionary classification of influenza genes into lineages is a first step in understanding their molecular epidemiology and can inform the subsequent implementation of control measures. We introduce a novel approach called Lineage Assignment By Extended Learning (LABEL) to rapidly determine cladistic information for any number of genes without the need for time-consuming sequence alignment, phylogenetic tree construction, or manual annotation. Instead, LABEL relies on hidden Markov model profiles and support vector machine training to hierarchically classify gene sequences by their similarity to pre-defined lineages. We assessed LABEL by analyzing the annotated hemagglutinin genes of highly pathogenic (H5N1) and low pathogenicity (H9N2) avian influenza A viruses. Using the WHO/FAO/OIE H5N1 evolution working group nomenclature, the LABEL pipeline quickly and accurately identified the H5 lineages of uncharacterized sequences. Moreover, we developed an updated clade nomenclature for the H9 hemagglutinin gene and show a similarly fast and reliable phylogenetic assessment with LABEL. While this study was focused on hemagglutinin sequences, LABEL could be applied to the analysis of any gene and shows great potential to guide molecular epidemiology activities, accelerate database annotation, and provide a data sorting tool for other large-scale bioinformatic studies

DOES THE GUT MICROBIOTA INFLUENCE THE IMMUNE RESPONSE TO INFLUENZA VACCINATION IN OBESE POPULATIONS?

In the United States and globally, rates of obesity have greatly increased and prevalence is continuing to grow. As of 2014, 35.0% of adult males and 40.4% of adult females in the U.S. were obese, according to the National Health and Nutrition Examination Survey (NHANES).1 Previous research in the Beck lab has demonstrated that vaccinated obese individuals have twice the likelihood of developing influenza or “flu-like-illness” when compared to healthy weight individuals, despite antibody levels above the threshold of protection.2 This is concerning because the influenza vaccination is the single most effective method of protection. What is not understood is the mechanisms underlying this difference in obese individuals. Previous work in the Beck Lab has found that antibody response is not significantly different between obese and lean individuals 30 days post vaccination, but falters between that time and one-year post vaccination. We have also found that there is a difference in the metabolism and function of T cells in the response to the influenza vaccination. With the goal of discovering new research directions that might aid in the discovery of molecular mechanisms underlying this phenomenon, I conducted a study using a bioinformatics approach to analyze the gut microbiota as a potential mediator. Strong preliminary evidence was found linking BMI, Firmicutes, and the antibody response to the 2014-2015 TIV immunization.Master of Scienc