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Yi-Ping Phoebe Chen
Researcher at La Trobe University
Publications - 287
Citations - 5400
Yi-Ping Phoebe Chen is an academic researcher from La Trobe University. The author has contributed to research in topics: Computer science & Feature selection. The author has an hindex of 33, co-authored 268 publications receiving 4206 citations. Previous affiliations of Yi-Ping Phoebe Chen include Fujian Agriculture and Forestry University & Deakin University.
Papers
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Journal ArticleDOI
Finding edging genes from microarray data.
TL;DR: This work proposes an algorithm to effectively find edging genes (EGs) in microarray space that prunes irrelevant patterns at earlier stages, time and space complexities are much less prevalent than in the border-based algorithm.
Journal ArticleDOI
A novel explainable neural network for Alzheimer's disease diagnosis
TL;DR: Zhang et al. as discussed by the authors proposed an explainable framework consisting of a predictor and explainable tool to provide accurate diagnoses with intuitive visualization maps and prediction basis, where the predictor is designed by applying attention mechanisms to multi-scale features so as to learn and discover class discriminative latent representations that are close to each brain volume's label.
Journal ArticleDOI
Efficient Conversion of RNA Pseudoknots to Knot-Free Structures Using a Graphical Model
TL;DR: The pseudoknot removal problem was transformed into a circle graph maximum weight independent set (MWIS) problem, in which each MWIS represents a unique optimal deknotted structure and an existing circle graph MWIS algorithm was extended to report either single or all solutions.
Book ChapterDOI
A new indexing method for high dimensional dataset
TL;DR: This paper proposes a new indexing method based on the surface of dimensionality, and proves that the Pyramid tree technology is a special case of the method.
Journal ArticleDOI
Transformed domain convolutional neural network for Alzheimer's disease diagnosis using structural MRI
TL;DR: In this paper , the authors proposed a three-dimensional Jacobian domain convolutional neural network (JD-CNN) to diagnose Alzheimer's disease without the involvement of the landmark detection framework.