In this activity, we will explore real clinical data from breast cancer patients in **The Cancer Genome Atlas (TCGA)**. The goal is not only to learn R. The goal is to use R to ask scientific questions: What information is collected from cancer patients? How do we summarize a large clinical dataset? What kinds of missing or uncertain values appear in real biomedical data? How can clinical features help us understand breast cancer subtypes? Later in DREAM-High, we will connect this type of clinical information to **gene expression data**. That is where computational biology becomes especially powerful: we can ask how molecular patterns relate to patient and tumor characteristics.

A avaliação desse laboratório é sobre a precisão, completude e clareza de sua interpretação dos resultados. Vocês devem escolher uma base de dados de estudo para realizar suas análises ("data/base.csv") configurando o Rmd 'arquivo_dados: "data/base.csv

This animation presents Leiden subnetwork detection results for directed, weighted bilateral import trade among the set of countries identified as constant cases from 1993 to 2025, using IMF Direction of Trade Statistics. Subnet labels and colors are applied consistently across years to facilitate comparison. These labels are substantive interpretations of the detected communities rather than fixed algorithmic community numbers.

Questions from in Chapter 7, Moving Beyond Linearity, of the ISLR textbook. Class STA6543- Predictive Modeling.

100-200-300