HPTRMF: Collaborative Matrix Factorization-Based Prediction Method for LncRNA-Disease Associations Using High-Order Perturbation and Flexible Trifactor Regularization-Reference-Cited by-同舟云学术

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HPTRMF: Collaborative Matrix Factorization-Based Prediction Method for LncRNA-Disease Associations Using High-Order Perturbation and Flexible Trifactor Regularization

Published:2024-07-26 Issue: Volume: Page:
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Xie Guobo¹,Li Dayin¹^ORCID,Lin Zhiyi¹,Gu Guosheng¹,Li Weijun¹,Chen Ruibin¹,Liu Zhenguo²

Affiliation:

1. School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China

2. 2MD Department of Thoracic Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China

Funder

National Natural Science Foundation of China

Scientific and Technological Planning Project of Guangzhou City

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.4c01070

Reference45 articles.

1. Long non-coding RNAs and complex diseases: from experimental results to computational models

2. GAM-MDR: probing miRNA–drug resistance using a graph autoencoder based on random path masking

3. Joint deep autoencoder and subgraph augmentation for inferring microbial responses to drugs

4. Fusion of multi-source relationships and topology to infer lncRNA-protein interactions

5. Long non-coding RNA-HAGLR suppressed tumor growth of lung adenocarcinoma through epigenetically silencing E2F1

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