Methods for evaluating unsupervised vector representations of genomic regions.

ABSTRACT: Representation learning models have become a mainstay of modern genomics. These models are trained to yield vector representations, or embeddings, of various biological entities, such as cells, genes, individuals, or genomic regions. Recent applications of unsupervised embedding approaches have been shown to learn relationships among genomic regions that define functional elements in a genome. Unsupervised representation learning of genomic regions is free of the supervision from curated metadata and can condense rich biological knowledge from publicly available data to region embeddings. However, there exists no method for evaluating the quality of these embeddings in the absence of metadata, making it difficult to assess the reliability of analyses based on the embeddings, and to tune mo

SUBMITTER: Zheng G 

PROVIDER: S-EPMC11316252 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature