DeepPheno is a method for predicting gene-phenotype (HPO classes) associations from gene functional annotations (GO classes) and gene expression values.
This repository contains script which were used to build and train the DeepPheno model together with the scripts for evaluating the model's performance.
- The code was developed and tested using python 3.7.
- To install python dependencies run:
pip install -r requirements.txt
- To install deeppheno package:
pip install deeppheno
- https://bio2vec.cbrc.kaust.edu.sa/data/deeppheno/ - Here you can find the data used to train and evaluate our method.
- data.tar.gz - data folder with latest dataset
- data-cafa2.tar.gz - CAFA2 challenge dataset
- predictions.txt.gz - DeepPheno predictions for human genes
deeppheno --data-root <path to data folder> --in-file <input-file>
The scripts require GeneOntology and Human Phenotype Ontology in OBO Format.
- uni2pandas.py - This script is used to convert data from UniProt database format to pandas dataframe.
- data.py - This script is used to generate training and testing datasets.
- pheno.py - This script is used to train the model
- evaluate_*.py - The scripts are used to compute Fmax, Smin
- GeneDis.groovy - This script is used to compute semantic similarity between gene and disease phenotypes
- Download all the files from https://bio2vec.cbrc.kaust.edu.sa/data/deeppheno/data.tar.gz and place them into data folder
- run
python pheno.pyto start training the model
If you use DeepPheno for your research, or incorporate our learning algorithms in your work, please cite:
Maxat Kulmanov and Robert Hoehndorf. DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier. PLoS Computational Biology, 2020. https://doi.org/10.1371/journal.pcbi.1008453