Design, Implementation, and Evaluation of Online Bioinformatics and Neuroinformatics Labs

In recent years, online laboratories have become highly integrated into the educational process due to the development of distance learning tools as well as circumstances associated with the Covid-19 pandemic. As part of a master's degree program in bioinformatics and neuroinformatics, in the academic years 2020–2021 and 2021–2022, the mandatory module "Laboratory Education (LE)” included 9 labs which transitioned to online delivery. A questionnaire was administered to all participants examining their self-reported learning as well as their satisfaction with each lab, the educational material associated with each lab, as well as the facilitator in each lab. A total of 73 postgraduate students completed the questionnaire. According to the results, the overall satisfaction from each laboratory ranged from 3.94 to 4.49/5.00. Furthermore, there is a variety of values in self-reported learning ranging from 23 to 50/50. Finally, although 7 out of 10 students indicated they are satisfied with the distance structure of LE, 8 out of 10 say they prefer LE to be carried out with a physical presence in the labs. © 2023,International journal of online and biomedical engineering. All Rights Reserved.

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.