Installation — BioNeuralNet 1.0.4 documentation

# Installation

BioNeuralNet supports Python 3.10 and 3.11 in this beta release. Follow the steps below to set up BioNeuralNet and its dependencies.

1. Install BioNeuralNet via pip:

       pip install bioneuralnet

   This installs the core BioNeuralNet modules for GNN embeddings, subject representation, disease prediction (DPMON), and clustering.

2. Install PyTorch and PyTorch Geometric (Separately):

   BioNeuralNet relies on PyTorch and PyTorch Geometric for GNN operations:

       pip install torch torchvision torchaudio
       pip install torch_geometric

   For GPU-accelerated builds or other configurations visit the official sites:

   - PyTorch Installation Guide

   - PyTorch Geometric Installation Guide

   Select the appropriate build for your system (e.g., Stable, Linux, pip, Python, CPU).

3. (Optional) Install R and External Tools:

   If you plan to use SmCCNet for network construction:

   - Install R from The R Project.

       - Version 4.4.2 or higher is recommended.

   - Install the required R packages. Open R and run:

         if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
         install.packages(c("dplyr", "jsonlite"))
         BiocManager::install(c("impute", "preprocessCore", "GO.db", "AnnotationDbi"))
         install.packages("SmCCNet")
         install.packages("WGCNA")

4. Additional Notes for External Tools:

   Refer to the External Tools.

5. Next Steps:

   - Explore Tutorials and BioNeuralNet Demo: Quick Start Guide or TCGA-BRCA Dataset for end-to-end workflows and examples.