Open AccessEmbeddings of genomic region sets capture rich biological associations in lower dimensions
Author(s) -
Erfaneh Gharavi,
Aaron Gu,
Guangtao Zheng,
Jason P. Smith,
Hyun-Jae Cho,
Aidong Zhang,
Donald E. Brown,
Nathan C. Sheffield
Publication year - 2021
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btab439
Subject(s) - computer science , curse of dimensionality , word2vec , robustness (evolution) , representation (politics) , embedding , set (abstract data type) , data mining , data set , pattern recognition (psychology) , computational biology , artificial intelligence , biology , genetics , politics , political science , law , gene , programming language
Genomic region sets summarize functional genomics data and define locations of interest in the genome such as regulatory regions or transcription factor binding sites. The number of publicly available region sets has increased dramatically, leading to challenges in data analysis.