bedms 0.2.0

BEDMess attribute standardizer for metadata attribute standardization

BEDMS

BEDMS (BED Metadata Standardizer) is a tool desgined to standardize genomics and epigenomics metadata attributes according to user-selected schemas such as ENCODE, FAIRTRACKS and BEDBASE. BEDMS ensures consistency and FAIRness of metadata across different platforms. Additionally, users have the option to train their own standardizer model using a custom schema (CUSTOM), allowing for the standardization of attributes based on users' specific research requirements.

Installation

To install bedms use this command:

pip install bedms

or install the latest version from the GitHub repository:

pip install git+https://github.com/databio/bedms.git

Usage

Standardizing based on available schemas

To choose the schema you want to standardize according to, please refer to the HuggingFace repository. Based on the schema design .yaml files, you can select which schema best represents your attributes. In the example below, we have chosen encode schema.

from bedms import AttrStandardizer

model = AttrStandardizer(
    repo_id="databio/attribute-standardizer-model6", model_name="encode"
)
results = model.standardize(pep="geo/gse228634:default")

assert results

Training custom schemas

Training your custom schema is very easy with BEDMS. You would need two things to get started:

1. Training Sets
2. training_config.yaml

To instantiate TrainStandardizer class:

from bedms.train import AttrStandardizerTrainer

trainer = AttrStandardizerTrainer("training_config.yaml")

To load the datasets and encode them:

train_data, val_data, test_data, label_encoder, vectorizer = trainer.load_data()

To train the custom model:

trainer.train()

To test the custom model:

test_results_dict = trainer.test()

To generate visualizations such as Learning Curves, Confusion Matrices, and ROC Curve:

acc_fig, loss_fig, conf_fig, roc_fig = trainer.plot_visualizations() 

Where acc_fig is Accuracy Curve figure object, loss_fig is Loss Curve figure object, conf_fig is the Confusion Matrix figure object, and roc_fig is the ROC Curve figure object.

Standardizing based on custom schema

For standardizing based on custom schema, your model should be on HuggingFace. The directory structure should follow the instructions mentioned on HuggingFace.

from bedms import AttrStandardizer

model = AttrStandardizer(
    repo_id="name/of/your/hf/repo", model_name="model/name"
)
results = model.standardize(pep="geo/gse228634:default")

print(results) #Dictionary of suggested predictions with their confidence: {'attr_1':{'prediction_1': 0.70, 'prediction_2':0.30}}