rnaglib.tasks.RNAGo

class rnaglib.tasks.RNAGo(size_thresholds=(15, 500), **kwargs)

Predict the GO terms associated with the Rfam family of a given RNA chain. Of course, this task is solved by definition since families are constructed using covariance models. However, it can still test the ability of a model to capture characteristic structural features from 3D.

Task type: multi-class classification Task level: RNA-level

Parameters:

size_thresholds (tuple[int]) – range of RNA sizes to keep in the task dataset(default (15, 500))

__init__(size_thresholds=(15, 500), **kwargs)

Methods

__init__([size_thresholds])
add_feature(feature[, feature_level, is_input])	Shortcut to RNADataset.add_feature
add_representation(representation)
add_rna_to_building_list(all_rnas, rna)
compute_distances()
compute_metrics(all_preds, all_probs, all_labels)
compute_one_metric(preds, probs, labels)
create_dataset_from_list(rnas)	Computes an RNADataset object from the lists touched in add_rna_to_building_list
describe()	Get description of task dataset.
dummy_inference()
evaluate(model, loader)
from_scratch(size_thresholds)
from_zenodo()	Downloads the task dataset from Zenodo and loads it.
get_split_datasets([recompute])
get_split_loaders([recompute])
get_task_vars()	Specifies the FeaturesComputer object of the tasks which defines the features which have to be added to the RNAs (graphs) and nucleotides (graph nodes)
init_metadata([additional_metadata])	Initialize dictionary to hold key/value pairs to self.metadata.
load()	Load dataset and splits from disk.
post_process()	Computes sequence similarity between all atom pairs using CD-Hit
process()	Creates the task-specific dataset.
remove_redundancy()
remove_representation(representation_name)
set_datasets([recompute])	Sets the train, val and test datasets Call this each time you modify self.dataset.
set_loaders([recompute])	Sets the dataloader properties.
split(dataset)	Calls the splitter and returns train, val, test splits.
to_csv(path)	Write a single CSV with all task data.
write()	Save task data and splits to root.

Attributes

default_metric
default_splitter	Returns the splitting strategy to be used for this specific task.
dummy_model
input_var
name
target_var
task_id	Task hash is a hash of all RNA ids and node IDs in the dataset
version