Cell type-specific interpretation of noncoding variants using deep learning-based methods.

ABSTRACT: Interpretation of noncoding genomic variants is one of the most important challenges in human genetics. Machine learning methods have emerged recently as a powerful tool to solve this problem. State-of-the-art approaches allow prediction of transcriptional and epigenetic effects caused by noncoding mutations. However, these approaches require specific experimental data for training and cannot generalize across cell types where required features were not experimentally measured. We show here that available epigenetic characteristics of human cell types are extremely sparse, limiting those approaches that rely on specific epigenetic input. We propose a new neural network architecture, DeepCT, which can learn complex interconnections of epigenetic features and infer unmeasured data from any a

SUBMITTER: Sindeeva M 

PROVIDER: S-EPMC10041527 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature