Not able to use Bioconductor to get the barcode ID_REF gene name for use in exploring known genes of the top 41 genes found to play a role in predicting with 100% accuracy if a sample has MS or is healthy. But reviewed the process of transcription and translation of a protein from mRNA when transcribed from its triplet codon sets by transfer RNA at the ribosome. Gaps of 1-2 RNAs were unpaired to a codon so gsub wasn't the best choice, but these barcodes are fragments and maybe those are deletions, insertions, or translations in genes that are risk associated genes for MS, or better yet are found in those with MS.

This is a blog post using R codes to analyse precipitation rates measures in January in Ireland for the years 1850 - 2024. Interactive map of weather stations is included for user friendly experience. The R codes used are explained along with pattern discussion and limitations.

This project explores the hidden structures within global health data by focusing on 46 Asian countries. Using the WHO Life Expectancy dataset, I applied various unsupervised learning techniques to group countries based on metrics like life expectancy, schooling, and adult mortality. Key features of this report include: Data Selection: Analysis focused on the year 2014 to ensure data completeness for critical variables like Alcohol consumption and Schooling. Methodology: A comparison of partitioning methods (K-means and PAM) and Hierarchical Clustering (Ward's method) to identify stable country groupings. Advanced Visualization: Use of dimensionality reduction techniques—MDS, t-SNE, and UMAP—to project complex, multi-dimensional data into intuitive 2D maps. Findings: The results reveal three distinct clusters: a "High-Performing" group (e.g., Japan, Singapore), an "Emerging" middle-income group (e.g., China, Thailand), and a "Challenged" group (e.g., Afghanistan, Yemen).