PMID- 36115300
OWN - NLM
STAT- MEDLINE
DCOM- 20220923
LR  - 20220930
IS  - 1879-0534 (Electronic)
IS  - 0010-4825 (Linking)
VI  - 149
DP  - 2022 Oct
TI  - MHDMF: Prediction of miRNA-disease associations based on Deep Matrix 
      Factorization with Multi-source Graph Convolutional Network.
PG  - 106069
LID - S0010-4825(22)00775-2 [pii]
LID - 10.1016/j.compbiomed.2022.106069 [doi]
AB  - A growing number of works have proved that microRNAs (miRNAs) are a crucial 
      biomarker in diverse bioprocesses affecting various diseases. As a good 
      complement to high-cost wet experiment-based methods, numerous computational 
      prediction methods have sprung up. However, there are still challenges that exist 
      in making effective use of high false-negative associations and multi-source 
      information for finding the potential associations. In this work, we develop an 
      end-to-end computational framework, called MHDMF, which integrates the 
      multi-source information on a heterogeneous network to discover latent 
      disease-miRNA associations. Since high false-negative exist in the miRNA-disease 
      associations, MHDMF utilizes the multi-source Graph Convolutional Network (GCN) 
      to correct the false-negative association by reformulating the miRNA-disease 
      association score matrix. The score matrix reformulation is based on different 
      similarity profiles and known associations between miRNAs, genes, and diseases. 
      Then, MHDMF employs Deep Matrix Factorization (DMF) to predict the miRNA-disease 
      associations based on reformulated miRNA-disease association score matrix. The 
      experimental results show that the proposed framework outperforms highly related 
      comparison methods by a large margin on tasks of miRNA-disease association 
      prediction. Furthermore, case studies suggest that MHDMF could be a convenient 
      and efficient tool and may supply a new way to think about miRNA-disease 
      association prediction.
CI  - Copyright (c) 2022. Published by Elsevier Ltd.
FAU - Ai, Ning
AU  - Ai N
AD  - Peng Cheng Laboratory, Shenzhen 518005, China; School of Computer Science and 
      Engineering, Faculty of Innovation Engineering, Macau University of Science and 
      Technology, Avenida Wai Long, Taipa, China.
FAU - Liang, Yong
AU  - Liang Y
AD  - Peng Cheng Laboratory, Shenzhen 518005, China. Electronic address: 
      yongliangresearch@gmail.com.
FAU - Yuan, Hao-Laing
AU  - Yuan HL
AD  - School of Automation, Guangdong University of Technology, Guangzhou 510006, 
      China.
FAU - Ou-Yang, Dong
AU  - Ou-Yang D
AD  - School of Computer Science and Engineering, Faculty of Innovation Engineering, 
      Macau University of Science and Technology, Avenida Wai Long, Taipa, China.
FAU - Liu, Xiao-Ying
AU  - Liu XY
AD  - Computer Engineering Technical College, Guangdong Polytechnic of Science and 
      Technology, Zhuhai 519090, Guangdong, China.
FAU - Xie, Sheng-Li
AU  - Xie SL
AD  - Institute of Intelligent Information Processing, Guangdong University of 
      Technology, Guangzhou 510000, Guangdong, China.
FAU - Ji, Yu-Han
AU  - Ji YH
AD  - School of Computer Science and Engineering, Faculty of Innovation Engineering, 
      Macau University of Science and Technology, Avenida Wai Long, Taipa, China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20220905
PL  - United States
TA  - Comput Biol Med
JT  - Computers in biology and medicine
JID - 1250250
RN  - 0 (MicroRNAs)
SB  - IM
MH  - Algorithms
MH  - Computational Biology/methods
MH  - *MicroRNAs/genetics
OTO - NOTNLM
OT  - Deep matrix factorization
OT  - End-to-end framework
OT  - Graph convolutional networks
OT  - MiRNA-disease associations
OT  - Multi-source information
COIS- Declaration of Competing Interest The authors declare that they have no known 
      competing financial interests or personal relationships that could have appeared 
      to influence the work reported in this paper.
EDAT- 2022/09/18 06:00
MHDA- 2022/09/24 06:00
CRDT- 2022/09/17 18:28
PHST- 2022/05/06 00:00 [received]
PHST- 2022/07/31 00:00 [revised]
PHST- 2022/08/27 00:00 [accepted]
PHST- 2022/09/18 06:00 [pubmed]
PHST- 2022/09/24 06:00 [medline]
PHST- 2022/09/17 18:28 [entrez]
AID - S0010-4825(22)00775-2 [pii]
AID - 10.1016/j.compbiomed.2022.106069 [doi]
PST - ppublish
SO  - Comput Biol Med. 2022 Oct;149:106069. doi: 10.1016/j.compbiomed.2022.106069. Epub 
      2022 Sep 5.