Affiliation:
1. Breast Cancer Clinical Research Unit Centro Nacional de Investigaciones Oncológicas – CNIO Madrid Spain
2. Life and Health Sciences Research Institute (ICVS), School of Medicine University of Minho Braga Portugal
3. ICVS/3B's –PT Government Associate Laboratory Braga Portugal
Abstract
AbstractDimensionality reduction techniques are essential in analyzing large ‘omics’ datasets in biochemistry and molecular biology. Principal component analysis, t‐distributed stochastic neighbor embedding, and uniform manifold approximation and projection are commonly used for data visualization. However, these methods can be challenging for students without a strong mathematical background. In this study, intuitive examples were created using COVID‐19 data to help students understand the core concepts behind these techniques. In a 4‐h practical session, we used these examples to demonstrate dimensionality reduction techniques to 15 postgraduate students from biomedical backgrounds. Using Python and Jupyter notebooks, our goal was to demystify these methods, typically treated as “black boxes”, and empower students to generate and interpret their own results. To assess the impact of our approach, we conducted an anonymous survey. The majority of the students agreed that using computers enriched their learning experience (67%) and that Jupyter notebooks were a valuable part of the class (66%). Additionally, 60% of the students reported increased interest in Python, and 40% gained both interest and a better understanding of dimensionality reduction methods. Despite the short duration of the course, 40% of the students reported acquiring research skills necessary in the field. While further analysis of the learning impacts of this approach is needed, we believe that sharing the examples we generated can provide valuable resources for others to use in interactive teaching environments. These examples highlight advantages and limitations of the major dimensionality reduction methods used in modern bioinformatics analysis in an easy‐to‐understand way.
Subject
Molecular Biology,Biochemistry
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. The Role of Artificial Intelligence in Biofertilizer Development;Metabolomics, Proteomics and Gene Editing Approaches in Biofertilizer Industry;2024