SpanSeq: similarity-based sequence data splitting method for improved development and assessment of deep learning projects.

ABSTRACT: The use of deep learning models in computational biology has increased massively in recent years, and it is expected to continue with the current advances in the fields such as Natural Language Processing. These models, although able to draw complex relations between input and target, are also inclined to learn noisy deviations from the pool of data used during their development. In order to assess their performance on unseen data (their capacity to generalize), it is common to split the available data randomly into development (train/validation) and test sets. This procedure, although standard, has been shown to produce dubious assessments of generalization due to the existing similarity between samples in the databases used. In this work, we present SpanSeq, a database part

SUBMITTER: Ferrer Florensa A 

PROVIDER: S-EPMC11327874 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature