rnaglib.tasks

Task objects hold everything you need to feed a prediction model and evaluate its performance on a variety of tasks, as well as to easily implement your own tasks.

Abstract classes

Subclass these to create your own tasks.

Task(root[, recompute, splitter, debug, ...])	Abstract class for a benchmarking task using the rnaglib datasets.
ResidueClassificationTask([additional_metadata])
RNAClassificationTask([additional_metadata])

RNA-level Classification

These tasks take as input an RNA and predict a property of the whole molecule.

RNAGo(root[, size_thresholds])	Predict the Rfam family of a given RNA chain. This is a multi-class classification task.
BindingSite(root[, cutoff, size_thresholds])
BenchmarkBindingSite(root[, cutoff])

Residue-level Classification

These tasks take as input an RNA and predict a property of each residue of the molecule.

ChemicalModification(root[, size_thresholds])	Residue-level binary classification task to predict whether a given residue is chemically modified.
ProteinBindingSite(root[, size_thresholds])	Residue-level task.
InverseFolding(root[, size_thresholds, ...])
gRNAde(root[, size_thresholds])	This class is a subclass of InverseFolding and is used to train a model on the gRNAde dataset.

Substructure-level Classification

Predict properties of substructures of a whole molecule (e.g. binding sites)

LigandIdentification(root[, data_filename, ...])

Binding pocket-level task where the job is to predict the (small molecule) ligand which is the most likely to bind a binding pocket with a given structure