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Yifeng Li
Researcher at Brock University
Publications - 66
Citations - 1775
Yifeng Li is an academic researcher from Brock University. The author has contributed to research in topics: Sparse approximation & Matrix decomposition. The author has an hindex of 16, co-authored 61 publications receiving 1336 citations. Previous affiliations of Yifeng Li include University of Windsor & University of British Columbia.
Papers
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Journal ArticleDOI
A review on machine learning principles for multi-view biological data integration.
TL;DR: It is shown that Bayesian models are able to use prior information and model measurements with various distributions, and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.
Journal ArticleDOI
Enter the Matrix: Factorization Uncovers Knowledge from Omics
Genevieve Stein-O’Brien,Raman Arora,Aedín C. Culhane,Alexander V. Favorov,Lana X. Garmire,Casey S. Greene,Loyal A. Goff,Yifeng Li,Aloune Ngom,Michael F. Ochs,Yanxun Xu,Elana J. Fertig +11 more
TL;DR: Matrix factorization (MF) techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions as discussed by the authors, which can uncover new biological knowledge from diverse high-throughput omics data in applications ranging from pathway discovery to timecourse analysis.
Journal ArticleDOI
The non-negative matrix factorization toolbox for biological data mining
Yifeng Li,Alioune Ngom +1 more
TL;DR: A convenient MATLAB toolbox containing both the implementations of various NMF techniques and a variety of NMF-based data mining approaches for analyzing biological data is provided.
Journal ArticleDOI
Deep Feature Selection: Theory and Application to Identify Enhancers and Promoters
TL;DR: A deep feature selection (DFS) model is proposed that takes advantages of deep structures to model nonlinearity and conveniently selects a subset of features right at the input level for multiclass data.
Book ChapterDOI
Deep Feature Selection: Theory and Application to Identify Enhancers and Promoters
TL;DR: A deep feature selection model is proposed that takes advantages of deep structures to model non-linearity and conveniently selects a subset of features right at the input level for multi-class data and results show that this model outperforms elastic net in terms of size of discriminative feature subset and classification accuracy.