Most well-studied proteins are folded, meaning they have a defined three-dimensional shape that helps determine each ...

Astral's uv utility simplifies and speeds up working with Python virtual environments. But it has some other superpowers, too: it lets you run Python packages and programs without having to ...

AI-enabled prediction of PCa using urine-based liquid biopsy demonstrates accurate, rapid, and accessible early cancer detection, with consistent performance across disease grades. This non-invasive ...

Simplify complex datasets using Principal Component Analysis (PCA) in Python. Great for dimensionality reduction and visualization.

I was interested in getting back the PC coordinates from the incremental PCA step for the dataset I'm currently working with, but saw that the coordinates were not available in the pytorch object that ...

Mass spectrometry imaging (MSI) is constantly improving in spatial resolving power, throughput and mass resolution. Although beneficial, these improvements increase data set size and content. The ...

The algorithm identifies and tries to retain the most important patterns or features in a dataset while discarding less relevant information, making the data more manageable without losing its ...

Principal Components Analysis (PCA) is probably the most popular multivariate statistical technique for reducing data with many dimensions and, often, well-being indicators are reduced to a single ...