Making predictions using linear models

Linear models are commonly used in bioinformatics for prediction tasks due to their simplicity, interpretability, and ability to handle high-dimensional datasets. In bioinformatics, researchers often work with large datasets that have a large number of features (such as gene expression data or sequence data), making it challenging to analyze them with more complex models. Linear models offer a straightforward and computationally efficient way to analyze these datasets. Linear models can help researchers identify genes or genetic variants that are associated with a particular trait or disease. They can also be used in feature selection, which is an important step in bioinformatics data analysis. Feature selection aims to identify the most relevant features (genes, proteins, etc.) that are associated with the outcome of interest (disease, drug response, etc.). Linear models can be used to rank features based on their importance and select the most...