Jupyter Dock is a set of Jupyter Notebooks for performing molecular docking protocols interactively, as well as visualizing, converting file formats and analyzing the results.

数理生物学演習（２０１９年度）のサンプルコード・データ

Final year project experimenting with clustering and topological data analysis of scRNA-seq data using Python and R across two Jupyter notebooks

Daniel & Gabby's Notebook

Example notebooks of PyFlowBAT functionality.

A series of notebooks that serves as an introduction to computational/theoretical neuroscience.

A Google Colaboratory notebook to predict molecular disease mechanisms for a group of missense variants

Python notebooks for Mathematical modeling for Systems Biology

Common EDA, ML, and data visualization notebooks (More things will be added after my Ph.D. defense)

Notebooks/install scripts for computational aspects of RCSI RSS

Collection of Jupyter notebooks from Kaggle for Computational Biology laboratory work.

This repository contains Jupyter notebooks used to perform computational analysis of LAT1 (SLC7A5) cholesterol binding sites, as published in JCTC

Predict protein folding structures using ColabFold. Gain a deeper understanding of protein folding prediction with AlphaFold2 and MMseqs2. Run the Jupyter notebook on UCloud, learn to interpret results, predict protein structures of interest. Technical requirements provided. Enhance your knowledge of protein folding and AlphaFold2's principles. Fam

Official repository for the ComBINeS Meetup group. https://www.meetup.com/combines/. You can run the notebooks using the Data Hub Project or by manually installing all prerequisites. The Data Hub Project website is available at: https://data-hub.info service runs at: https://bio.data-hub.info - only a simple sing up is needed.