3.6.5 environment with the packages seaborn 0.7.1, scikit-learn 0.20.2, pymc3 3.5, pandas 0.23.4, numpy 1.14.3, matplotlib 3.0.2, scipy 1.1.0, PTMCMC Sampler 2015.2, and ART. The process is documented in a jupyter notebook, available at GitHub (https://github.com/sorpet/Zhang_and...
Grave is a graph visualization package combining ideas from Matplotlib, NetworkX, and seaborn. Its goal is to provide a network drawing API that covers the most use cases with sensible defaults and simple style configuration. Currently, it supports drawing graphs from NetworkX. Website (including ...
we integrate two tools, LIANA and Tensor-cell2cell, which, when combined, can deploy multiple existing methods and resources to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this work, ...
Additionally, Matplotlib and Seaborn were used for visualizations, and Scikit-Learn facilitated data normalization and scaling. All experiments were conducted using Kaggle’s free resources, leveraging GPUs for BiLSTM training and CPUs for SARIMA. SARIMA fitting, which does not benefit from GPU ...