Computational trajectory analysis is a key computational task for inferring differentiation trees from this single-cell data. An open challenge is the prediction of complex and multi-branching trees ...

We have refactored the entire library to make it easier to understand and use. To avoid installing extra dependencies for additional features, we have commented out the non-numpy dependencies. If you ...

NumPy is foundational for numerical data processing in Python, providing efficient multi-dimensional array objects essential for handling datasets. It supports fast mathematical and logical operations ...

Python is rapidly becoming the de facto standard language for systems integration. Python has a large user and developer-base external to the neuroscience community, and a vast module library that ...

Understanding the complex biology of mobile genetic elements (MGEs) is crucial for manipulating microbiomes and improving the treatment of microbiome-associated diseases. MGEs carried on plasmids can ...

If you're new to the world of machine learning and optimization, the term "Gradient Descent" might sound intimidating. However, don't let the name scare you away. Gradient Descent is a fundamental ...

Generating synthetic data is useful when you have imbalanced training data for a particular class, for example, generating synthetic females in a dataset of employees that has many males but few ...

cupyimg extends CuPy with additional functions for image/signal processing. This package implements a subset of functions from NumPy, SciPy and scikit-image with GPU support. These implementations ...