A framework for analyzing single-cell genomics data, in which geometrical properties are harnessed to obtain insights on cellular diversity, including precise clustering, clear visualizations, and ...

Top Python frameworks streamline the entire lifecycle of artificial intelligence projects from research to production. Modern Python tools enhance model performance, scalability, and deployment ...

Abstract: Dimensionality reduction methods are employed to decrease data dimensionality, either to enhance machine learning performance or to facilitate data visualization in two or three-dimensional ...

Design is an iterative process of creating a structure and telling a story. The structure provides utility, while the story fosters connection. Although the design process is dynamic, I often find ...

Causal inference has been increasingly essential in modern observational studies with rich covariate information. However, it is often challenging to estimate the causal effect with high-dimensional ...

Pandas is a robust data manipulation library that offers high-performance, user-friendly data structures and analytical tools in Python. Pandas enables users to import, clean, transform, and analyze ...

Background: Linear dimensionality reduction techniques are widely used in many applications. The goal of dimensionality reduction is to eliminate the noise of data and extract the main features of ...