Table of Contents
This repo contains the code implementation of my Thesis "Intrinsically disordered protein predictionfor genomes and metagenomes".
This section should list any major frameworks/libraries used to bootstrap your project. Leave any add-ons/plugins for the acknowledgements section. Here are a few examples.
This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.
Below is an example of how you can instruct your audience on installing and setting up your app. This template doesn't rely on any external dependencies or services.
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Get a free API Key at https://example.com
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Clone the repo
git clone https://github.com/iliasprc/IDPMetagenome.git
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Install package requirements
pip install -r requirements.txtUse this space to show useful examples of how a project can be used. Additional screenshots, code examples and demos work well in this space. You may also link to more resources.
For more examples, please refer to the Documentation
python trainv2.py -c config/config_parse.yml
Arguments for training
cwd: /home/ # working directory
logger: IDP # logger name
epochs: 30 # number of training epochs
seed: 123 # randomness seed
cuda: True # use nvidia gpu
gpu: 0 # id of gpu
save: True # save checkpoint
batch_size: 1
dim: 128
layers: 2
heads: 2
load: False # load pretrained checkpoint
gradient_accumulation: 8 # gradient accumulation steps
pretrained_cpkt: None
log_interval: 1000 # print statistics every log_interval
model: idprnn # model name
pretrained: False
optimizer: AdamW # optimizer type
lr: 1e-5 # learning rate
weight_decay: 0.00001 # weight decay
scheduler: ReduceLRonPlateau # type of scheduler
scheduler_factor: 0.8 # learning rate change ratio
scheduler_patience: 3 # patience for some epochs
scheduler_min_lr: 1e-5 # minimum learning rate value
scheduler_verbose: 5e-6 # print if learning rate is changed
dataset_type: classification
num_workers: 2
shuffle: True # shuffle samples after every epoch
dataset: MXD494
input_data: data_dir
name: MXD494 # dataset name
use_elmo: False
train_augmentation: True # do augmentation
val_augmentation: False
Intrinsic disorder prediction results on several datasets with Transformer-based methods. ESM-t6-43m and ESM-t12-85m were used pretrained from Evolutionary Scale Modeling paper for best results.
- Add Changelog
- Add back to top links
- Add Additional Documentation
- Add additional methods for IDP
See the open issues for a full list of proposed features (and known issues).
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE.txt for more information.
Ilias Papastratis
Project Link: IDPMetagenome
Use this space to list resources you find helpful and would like to give credit to. I've included a few of my favorites to kick things off!