Gene encoder: a feature selection technique through unsupervised deep learning-based clustering for large gene expression data

© 2020, Springer-Verlag London Ltd., part of Springer Nature. Cancer is a severe condition of uncontrolled cell division that results in a tumor formation that spreads to other tissues of the body. Therefore, the development of new medication and treatment methods for this is in demand. Classification of microarray data plays a vital role in handling such situations. The relevant gene selection is an important step for the classification of microarray data. This work presents gene encoder, an unsupervised two-stage feature selection technique for the cancer samples’ classification. The first stage aggregates three filter methods, namely principal component analysis, correlation, and spectral-based feature selection techniques. Next, the genetic algorithm is used, which evaluates the chromosome utilizing the autoencoder-based clustering. The resultant feature subset is used for the classification task. Three classifiers, namely support vector machine, k-nearest neighbors, and random forest, are used in this work to avoid the dependency on any one classifier. Six benchmark gene expression datasets are used for the performance evaluation, and a comparison is made with four state-of-the-art related algorithms. Three sets of experiments are carried out to evaluate the proposed method. These experiments are for the evaluation of the selected features based on sample-based clustering, adjusting optimal parameters, and for selecting better performing classifier. The comparison is based on accuracy, recall, false positive rate, precision, F-measure, and entropy. The obtained results suggest better performance of the current proposal

Motivators for the public to receive the seasonal influenza vaccination and the effect of coronavirus disease 2019 pandemic on the uptake of the seasonal influenza vaccination

BACKGROUND: The coverage of the seasonal influenza vaccine has always been sub-par. Understanding the motivators of receiving vaccines, especially during pandemics, could enhance and increase the coverage rates. The Saudi Ministry of Health launched its annual influenza vaccination campaign during the 2021 influenza season and provided vaccinations in primary healthcare settings. This study aims to explore public motivators to receive influenza vaccination, particularly during the coronavirus disease 2019 global pandemic. MATERIALS AND METHODS: This cross-sectional study enrolled 783 participants who attended the influenza vaccination campaign. All persons who received the influenza vaccine in the influenza vaccination campaign held in Dammam, Saudi Arabia, from October to November 2021, were interviewed and completed a self-administered questionnaire. Odds ratio with a 95% confidence interval were estimated using the full model fit . The significance level was set as α = 0.05. RESULTS: The majority of the participants had more than high school education (81%), were males (66.2%), and married (59.6%), and 50.6% were below 35 years of age. Participants with higher education, health-care workers, and those who had been previously counseled on influenza vaccination were less likely to have started taking the influenza vaccination, whereas smokers and persons who do not have routine checkups were more likely to start influenza vaccination. The main motivator to take the influenza vaccine was the establishment of a vaccination campaign near the participant's workplace (62.2%), followed by advice from their physician (30.3%), and fear of having influenza disease (29.6%). CONCLUSION: Accessibility to the vaccination campaigns was the main motivator for receiving the vaccine followed by the advice from physician. Advice from physician and increasing mobile vaccination campaigns and mobile clinics would substantially increase influenza vaccine uptake