Network Inference flow (NIflow)-a Python toolbox for network inference built on top of Tensorflow
Aim: We aim to create a toolbox for network inference with controlled network and data properties in Python, similar to the MATLAB toolbox GeneSPIDER.
Social/scientific motivation: We want to make a single, freely available, open source toolbox covering all aspects of the network inference workflow that takes advantage of the computational scalability of Tensorflow to advance scientific discovery.
Scientific background: A handful of toolboxes for network inference exist today, but none in Python. We will largely base our work on the MATLAB toolbox by Andreas Tjärnberg, Daniel Morgan, Matthew Studham, Torbjörn Nordling, and Erik Sonnhammer. For more information see Tjärnberg et al. ”GeneSPIDER – gene regulatory network inference benchmarking with controlled network and data properties”, Molecular BioSystems, 2017, 13, 1304.
Justin’s motivation: I like this project because it enables me to learn coding in Python and MATLAB, Tensorflow, network inference, how to automatically generate UML diagrams, how gene regulatory networks function, and contribute to making these tools available.