Model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data.

ABSTRACT:

Background

It remains an important challenge to predict the functional consequences or clinical impacts of genetic variants in human diseases, such as cancer. An increasing number of genetic variants in cancer have been discovered and documented in public databases such as COSMIC, but the vast majority of them have no functional or clinical annotations. Some databases, such as CiVIC are available with manual annotation of functional mutations, but the size of the database is small due to the use of human annotation. Since the unlabeled data (millions of variants) typically outnumber labeled data (thousands of variants), computational tools that take advantage of unlabeled data may improve prediction accuracy.

Result

To leverage unlabeled data to predict functional importance

SUBMITTER: Ren Z 

PROVIDER: S-EPMC9909865 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature