J
Jiuyong Li
Researcher at University of South Australia
Publications - 335
Citations - 6808
Jiuyong Li is an academic researcher from University of South Australia. The author has contributed to research in topics: Computer science & Association rule learning. The author has an hindex of 38, co-authored 285 publications receiving 5280 citations. Previous affiliations of Jiuyong Li include Kunming University of Science and Technology & Griffith University.
Papers
More filters
Proceedings ArticleDOI
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
TL;DR: It is proved that the optimal (α, k)-anonymity problem is NP-hard, and a local-recoding algorithm is proposed which is more scalable and result in less data distortion.
Journal ArticleDOI
Identifying miRNAs, targets and functions
TL;DR: This review focuses on computational methods of inferring miRNA functions, including miRNA functional annotation and inferringMiRNAs regulatory modules, by integrating heterogeneous data sources and briefly introduces the research in miRNA discovery and miRNA-target identification.
Journal ArticleDOI
CancerSubtypes: an R/Bioconductor package for molecular cancer subtype identification, validation and visualization.
Taosheng Xu,Thuc Duy Le,Lin Liu,Su Ning,Rujing Wang,Bingyu Sun,Antonio Colaprico,Gianluca Bontempi,Jiuyong Li +8 more
TL;DR: CancerSubtypes is an R package for identifying cancer subtypes using multi‐omics data, including gene expression, miRNA expression and DNA methylation data that provides a standardized framework for data pre‐processing, feature selection, and result follow‐up analyses.
Journal ArticleDOI
Discover Dependencies from Data—A Review
TL;DR: This paper reviews the methods for functional dependency, conditional Functional Dependency, approximate functional Dependence, and inclusion dependency discovery in relational databases and a method for discovering XML functional dependencies.
Journal ArticleDOI
Kernel Discriminant Learning for Ordinal Regression
TL;DR: This paper proposes a novel regression method by extending the Kernel Discriminant Learning using a rank constraint and demonstrates experimentally that the proposed method is capable of preserving the rank of data classes in a projected data space.