ReviewInformaticsThe application of principal component analysis to drug discovery and biomedical data

- Principal Component Analysis provides a general frame for systemic approaches in pharmacology.
- Principal components correspond to the latent factors of a data set.
- Network pharmacology implies a non-reductionist approach to drug discovery.
- PCA allows to estimate the amount of order in biological systems.

There is a neat distinction between general purpose statistical techniques and quantitative models developed for specific problems. Principal Component Analysis (PCA) blurs this distinction: while being a general purpose statistical technique, it implies a peculiar style of reasoning. PCA is a 'hypothesis generating' tool creating a statistical mechanics frame for biological systems modeling without the need for strong a priori theoretical assumptions. This makes PCA of utmost importance for approaching drug discovery by a systemic perspective overcoming too narrow reductionist approaches.