An ensemble learning based method for training Convolutional Neural Networks on OTU data after stratification based on phyla. This is a deep learning methodology to arrange OTU data for CNN modelling based on similarity between OTUs in phylum level of the taxonomy tree.
Three datasets are used: 1) Simulation study 2) T2D study by Qin et al., 2012 and 3) Cirrhosis study by Qin et al., 2014. The files NN_Sim.py, NN_T2D.py and NN_Cirr.py are the main files. Relative abundance in OTUs are present in rows for each individual in the files Sim_OTU.csv, T2D_OTU.csv and Cirr_OTU.csv. The datasets are stored in T2D.zip, Cirrhosis.zip and Simulation Data.zip.
Prerequisites
- Python 2.7
- CUDA
- cuDNN
- Conda
- TensorFlow
- NumPy pandas
- Keras
Citation
If you find our work useful in your research, please cite our work:
TaxoNN: Ensemble of Neural Networks on Stratified Microbiome Data for Disease Prediction, Bioinformatics, May 2020
Divya Sharma, Andrew D Paterson, Wei Xu, TaxoNN: ensemble of neural networks on stratified microbiome data for disease prediction, Bioinformatics, Volume 36, Issue 17, 1 September 2020, Pages 4544–4550, https://doi.org/10.1093/bioinformatics/btaa542
Bibtex:
@article{sharma2020taxonn,
title={TaxoNN: Ensemble of Neural Networks on Stratified Microbiome Data for Disease Prediction},
author={Sharma, Divya and Paterson, Andrew D and Xu, Wei},
journal={Bioinformatics},
volume = {36},
number = {17},
pages = {4544-4550},
year={2020},
month = {05}
}