A Computational Method for Classifying Different Human Tissues with Quantitatively Tissue-Specific Expressed Genes.

ABSTRACT: Tissue-specific gene expression has long been recognized as a crucial key for understanding tissue development and function. Efforts have been made in the past decade to identify tissue-specific expression profiles, such as the Human Proteome Atlas and FANTOM5. However, these studies mainly focused on "qualitatively tissue-specific expressed genes" which are highly enriched in one or a group of tissues but paid less attention to "quantitatively tissue-specific expressed genes", which are expressed in all or most tissues but with differential expression levels. In this study, we applied machine learning algorithms to build a computational method for identifying "quantitatively tissue-specific expressed genes" capable of distinguishing 25 human tissues from their expression patterns. Our res

SUBMITTER: Li J 

PROVIDER: S-EPMC6162521 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature