Greetings! I'm delighted to share my insights and experiences with you in the realm of transcriptomics, immune responses, and machine learning. When comparing the performance of random forests and decision trees in predicting immune responses using transcriptome data, I've found the following performance metrics to be most informative:

When working with complex gene expression data, it's essential to consider the biological relevance of the performance metrics. For instance, in some cases, a high accuracy might not necessarily translate into biologically meaningful results. Instead, focus on identifying models that capture the underlying biology effectively, such as those that distinguish between different immune cell subtypes or predict functional pathways.

To optimize model parameters and prevent overfitting, follow these best practices:

By considering these performance metrics, biological relevance, and optimization strategies, you'll be well on your way to developing robust and reliable machine learning models for predicting immune responses using transcriptome data.

Good luck with your research endeavors!