✋ArmNet

[CURRENTLY UNDER CONSTRUCTION]

Here we present a hybrid transformer and convolutional network for predicting RNA reactivities based on data provided in Stanford Ribonanza RNA Folding competition.

Our approach took the first place in the Ribonanza competition. In this repository we present our post-competition model based on methods used by other contestants and our insights.
[Ribonanza Paper Preprint]

👩‍💻Model weights

The ArmNet weights are available at Kaggle.

🏃‍♀️Training the model

First, download data from Stanford Ribonanza RNA Folding competition and then specify data paths in config file. If you want to train model which takes BPPMs (base pair probability matrices) as input you should do the fllowing:

• for train matrices merge all matrices in file named "joined.mmap" using numpy.memmap and save array of corresponding sequence IDs in file named "index.ind" using numpy.save
• for test matrices save individual matrices using numpy.save in separate directory; these files should be named as "SEQUENCE_ID.npy", SEQUENCE_ID is corresponding sequence's ID

For each sequence in train and test datasets we predicted BPPM using EternaFold package.

All data paths along with main model parameters are specified in config.json file. Architectural parameters can be changed only through config.json, whereas training parameters can be changed as command line arguments. By default, in each run all scripts save config-file with all parameters used.

The examples of running training script:

python train.py -c CONFIG.json

# Output directory should be specified as command line argument; be default all output files are saved in directory "results"
python train.py -c CONFIG.json --out_dir_path ./chosen_dir

# Training parameters can be conviniently changed in command line
# Values specified in command line will be used instead of corresponding values from config-file
# By default, all changes are saved in separate config file in ouput directory 
python train.py -c CONFIG.json --out_dir_path ./chosen_dir --device 0 --max_lr 0.002

🕵️‍♂️Testing on competition data

Analogously to training script, test.py uses config file, but it requires model weights specified in --pretrained_model_weights argument. This script generates contest submission.

The examples of running testing script:

python test.py -c CONFIG.json --pretrained_model_weights ./results/sgd_run/model.pth

To convert submisiion file to dataframe of format equivalent to training dataset you can use to_train_format function from test_utils.py