/DeepHF

Core code for the DeepHF prediction tool

Primary LanguageJupyter Notebook

System requirements

The code were tesed on Linux and Mac OS systems.

Note:

  • Keras should be run with tensorflow as its backend.
  • ViennaRNA, a C code library for prediction of RNA secondary structure, needs to be downloaded before installation.

The required software/packages are:

  • python=3.6.5
  • numpy=1.14.0
  • scipy=1.0.0
  • h5py=2.7.1
  • tensorflow=1.8.0
  • keras=2.1.6
  • scikit-learn=0.19.1
  • biopython=1.71
  • viennarna=2.4.5
  • matplotlib
  • DotMap
  • GPyOpt
  • pandas

It is worth noting that when the computing environment(e.g, the version of tensorflow or biopython) changes, the prediction results might change slightly, but the main conclusion won't be affected.

Installation Guide

conda create -n crispr python=3.6.5 ipykernel matplotlib pandas numpy=1.14.0 scipy=1.0.0 h5py=2.7.1 tensorflow=1.8.0 keras=2.1.6 scikit-learn=0.19.1 biopython=1.71 viennarna=2.4.5
pip install GPyOpt
pip install DotMap
ipython kernel install --user --name crispr --display-name "Python3(crispr)"

Installation time depends on your own network environment.

Demo

Demos were included in the Demo.ipynb file. It contains prediction, metrics and model training demos.

Hyperparameters Searching

HyperParametersSearching.ipynb, HyperParameters searahcing demonstrates how to get the optimized hyperprameters of DeepHF model.It is time consuming.

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