GAERF: predicting lncRNA-disease associations by graph auto-encoder and random forest
Sign Up to like & getrecommendations! Published in 2021 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbaa391
Abstract: Predicting disease-related long non-coding RNAs (lncRNAs) is beneficial to finding of new biomarkers for prevention, diagnosis and treatment of complex human diseases. In this paper, we proposed a machine learning techniques-based classification approach to identify… read more here.
Keywords: auto encoder; lncrna disease; disease associations; disease ... See more keywords
Predicting potential interactions between lncRNAs and proteins via combined graph auto-encoder methods
Sign Up to like & getrecommendations! Published in 2022 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbac527
Abstract: Long noncoding RNA (lncRNA) is a kind of noncoding RNA with a length of more than 200 nucleotide units. Numerous research studies have proven that although lncRNAs cannot be directly translated into proteins, lncRNAs still… read more here.
Keywords: combined graph; lncrnas proteins; auto encoder; graph auto ... See more keywords
Embedding Graph Auto-Encoder for Graph Clustering.
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2022.3158654
Abstract: Graph clustering, aiming to partition nodes of a graph into various groups via an unsupervised approach, is an attractive topic in recent years. To improve the representative ability, several graph auto-encoder (GAE) models, which are… read more here.
Keywords: auto encoder; relaxed means; graph; graph clustering ... See more keywords