Predicting multiple types of miRNA-disease associations using adaptive weighted nonnegative tensor factorization with self-paced learning and hypergraph regularization
Sign Up to like & getrecommendations! Published in 2022 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbac390
Abstract: More and more evidence indicates that the dysregulations of microRNAs (miRNAs) lead to diseases through various kinds of underlying mechanisms. Identifying the multiple types of disease-related miRNAs plays an important role in studying the molecular… read more here.
Keywords: paced learning; tensor; hypergraph regularization; self paced ... See more keywords
Self-Weighting and Hypergraph Regularization for Multi-view Spectral Clustering
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2020.3011599
Abstract: Leveraging the consensus and complementary principle to find a common representation for different views is an essential problem of multi-view clustering. To address the problem, many Low-Rank Representation (LRR) based methods have been proposed. However,… read more here.
Keywords: hypergraph regularization; multi view; view; self weighting ... See more keywords
SPLHRNMTF: robust orthogonal non-negative matrix tri-factorization with self-paced learning and dual hypergraph regularization for predicting miRNA-disease associations
Sign Up to like & getrecommendations! Published in 2024 at "BMC Genomics"
DOI: 10.1186/s12864-024-10729-w
Abstract: MicroRNAs (miRNAs) have been demonstrated to be closely related to human diseases. Studying the potential associations between miRNAs and diseases contributes to our understanding of disease pathogenic mechanisms. As traditional biological experiments are costly and… read more here.
Keywords: disease; self paced; matrix; mirna disease ... See more keywords