Upon completing Module 2, students will be able to:

 

• Apply bioinformatic tools using R to preprocess, analyze, and visualize high-throughput biological data.

 

• Choose the most suitable data visualization techniques based on dataset type and research context.

 

• Interpret graphical representations of omics data (e.g., expression plots, heatmaps, PCA) to extract biologically meaningful insights in the context of liver disease research.

 

• Perform downstream analyses such as differential expression, pathway enrichment, and gene ontology to interpret the biological significance of omics data.

 

• Identify potential biomarkers or predictors of clinical outcomes using statistical and machine learning approaches.