CAFA PI Challenge
Code for the CAFA PI challenge.
All code, unless otherwise specified, runs with Python 3.
To install Python dependencies with pip, run:
pip3 install pandas tensorflow sklearn # Dask needs to be upgraded for tensorflow to work pip3 install dask --upgrade
Generating the parsed data
Download the data files:
And parse them:
The parsed data file is about 240 MB, so it's better to parse it on your own machine than to store in source control.
To parse it, run the following from the project root:
Simulated data for testing
This data isn't actually useful for training a model, but may be useful in testing whether a model works on very simple data. The script's random seed is set so that this data will be the same each time it's generated.
To generate the simulated data, run the following from the project root:
python3 -m ml.simulate_data
The data can then be found in
Loading the data
In order to load a csv into a format usable by TensorFlow, run the following in Python:
from ml.embeddings import load_data data, targets = load_data("./data/example/train_fake.csv")
Running the Model
tensorboard --logdir log/
Then go to http://127.0.0.1:6006 in your browser to see the results.
You may need to delete the
log/ directory before running the model a second time in order to clear old results.
python3 -m ml.cnn
- Ashton, Calvin, Dan
So far we have collected all available protein data for Pseudemonas and the yeast, as well as GO annotations for these proteins. Summary statistics of these datasets are available in the README.txt file in the data_acquisition directory. We also have a script that retrieves the JSON data for a given GO term. We are currently working on a script to generate summary statistics of protein sequences, a script to update GO term to a relevant parent term, and also to find more proteins with our desired annotations to enrich our dataset.
- Dane, Dallas
- Gage, Ben
- Kimball, Daniel
- Jonathan, Erica