@article{,
title= {regnet.pkl},
journal= {},
author= {Liu ZP, Wu C, Miao H, Wu H.},
year= {},
url= {http://www.regnetworkweb.org},
abstract= {In this work, we build a knowledge-based database, named 'RegNetwork', of gene regulatory networks for human and mouse by collecting and integrating the documented regulatory interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes from 25 selected databases. Moreover, we also inferred and incorporated potential regulatory relationships based on transcription factor binding site (TFBS) motifs into RegNetwork. As a result, RegNetwork contains a comprehensive set of experimentally observed or predicted transcriptional and post-transcriptional regulatory relationships, and the database framework is flexibly designed for potential extensions to include gene regulatory networks for other organisms in the future.

},
keywords= {machine learning, Graph, Transcriptomics, Computational Biology, Gene Expression, Genomics, Graph Convolutions},
terms= {},
license= {},
superseded= {}
}