a hybrid convolutional and recurrent neural network for compressing compression human mitochondrial genomes
deepDNA, a novel unified model called deepDNA that combines the convolutional neural network (CNN) with the long short-term memory network (LSTM) for compressing human mitochondrial genome sequences. The experiment has shown that out method deepDNA is able to learn sequence local features through a convolutional layer, and to learn higher level representations of long-term sequences dependencies through a long short-term memory network (LSTM) layer. We evaluated the learned genome sequences representations model on human mitochondrial genome sequences compressing tasks and achieved a satisfactory result.
This is a step by step instruction for installing the deepDNA for python 2.7*.
- TensorFlow >= 1.9.0
- Keras >= 2.2.0
- biopython >= 1.72
To verify the validity of our method, 1000 human complete mitochondrial sequences were downloaded from MITOMAP (http://www.mitomap.org) database.
We experimented our method deepDNA on 1000 human complete mitochondrial genome sequences and random split it into three datasets: 70% training set, 20% validation set and 10% test set.
Data processing file. It randomly select 1000 human complete mitochondrial genome sequences from downloaded MITOMAP dataset and random split it into three parts (70% training set, 20% validation set and 10% test set) and save them into files.
Train the deepDNA model parameters using training dataset.
Test the deepDNA model using test dataset.
This project is licensed under the MIT License - see the LICENSE file for details
If you have any question, please contact the author rjwang.hit@gmail.com