PseudoFuN is a novel database and query tool for homologous PseudoGene and coding Gene (PGG) families. It supports dynamic search, graphical visualization and functional analysis of pseudogenes and coding genes based on the PGG families. This work sets a start point for functional analysis of potentially regulatory pseudogenes.
PseudoFuN DB Search | Supporting Data | Read Me (Posted 3/28/2019)
Graph2GO is a graph representation learning method for predicting protein function on Gene Ontology terms. We pre-trained our model and support queries for around 15,000 human proteins. Our web server is based on the shiny app in R.
Graph2GO Search | Supporting Data | Source Code (Posted 6/15/2020)
Pseudo2GO is a graph-based deep learning semi-supervised model for pseudogene function prediction. Besides sequence similarity used for constructing the backbone of the graph, multiple features are incorporated into the model, including expression profiles, interactions with microRNAs, protein-protein interactions (PPIs), and genetic interactions.
Source Code (Posted 8/19/2020)