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Predicting miRNA-Disease Association Based on Neural Inductive Matrix Completion with Graph Autoencoders and Self-Attention Mechanism

Chen Jin, Zhuangwei Shi, Ken Lin, Han Zhang
2022 Biomolecules  
In this paper, we propose a Neural Inductive Matrix completion-based method with Graph Autoencoders (GAE) and Self-Attention mechanism for miRNA-disease associations prediction (NIMGSA).  ...  This neural inductive matrix completion-based method is also an implementation of self-attention mechanism for miRNA-disease associations prediction.  ...  [43] implemented a neural inductive matrix completion algorithm based on GCN [45] .  ... 
doi:10.3390/biom12010064 pmid:35053212 pmcid:PMC8774034 fatcat:2dyhknngkrhyrif2zrzwnd666y

Stable solution to l 2,1-based robust inductive matrix completion and its application in linking long noncoding RNAs to human diseases

Ashis Kumer Biswas, Dongchul Kim, Mingon Kang, Chris Ding, Jean X. Gao
2017 BMC Medical Genomics  
Kytai Nguyen in Bioeengineering Department at University of Texas at Arlington for the comments and feedback on our lincRNAs discovery results. Funding Not applicable.  ...  Availability of data and materials The ChIP-base dataset is available at https://omictools.com/chipbase-tool. The Linc2GO dataset is available at: https://omictools.com/linc2go-tool.  ...  Inductive Matrix Completion (IMC) based algorithms utilize side information about both the lincRNAs and diseases along with the known association evidences to predict missing associations [8] .  ... 
doi:10.1186/s12920-017-0310-1 pmid:29297358 pmcid:PMC5751820 fatcat:ivgocpfks5ajphcka7d6jv3naa

Inductive Matrix Completion Based on Graph Neural Networks [article]

Muhan Zhang, Yixin Chen
2020 arXiv   pre-print
In this paper, we propose an Inductive Graph-based Matrix Completion (IGMC) model to address this problem.  ...  IGMC trains a graph neural network (GNN) based purely on 1-hop subgraphs around (user, item) pairs generated from the rating matrix and maps these subgraphs to their corresponding ratings.  ...  In this paper, we propose a novel inductive matrix completion method that does not use any content.  ... 
arXiv:1904.12058v3 fatcat:bodp3t4hencl5fjck4ykj5yflu

How Neural Processes Improve Graph Link Prediction [article]

Huidong Liang, Junbin Gao
2021 arXiv   pre-print
While most of the literature focuses on transductive link prediction that requires all the graph nodes and majority of links in training, inductive link prediction, which only uses a proportion of the  ...  Link prediction is a fundamental problem in graph data analysis.  ...  based on the existing graph and hence performing inductive link prediction.  ... 
arXiv:2109.14894v1 fatcat:xgx3lqbakraixo5xxf7q3ftdcy

Inferring Latent Disease-lncRNA Associations by Faster Matrix Completion on a Heterogeneous Network

Wen Li, Shulin Wang, Junlin Xu, Guo Mao, Geng Tian, Jialiang Yang
2019 Frontiers in Genetics  
A novel method named faster randomized matrix completion for latent disease-lncRNA association prediction (FRMCLDA) has been proposed by virtue of improved randomized partial SVD (rSVD-BKI) on a heterogeneous  ...  In this study, a computational prediction model has been remodeled as a matrix completion framework of the recommendation system by completing the unknown items in the rating matrix.  ...  A computational model named SIMCLDA is designed to predict latent disease-lncRNA relationships, taking advantage of the inductive matrix completion (IMC) method (Lu et al., 2018) .  ... 
doi:10.3389/fgene.2019.00769 pmid:31572428 pmcid:PMC6749816 fatcat:hkcy5puttvgf7bmvkyg6smxj64

FPGA-based Design and Implementation of Real-time Robot Motion Planning

Ruige Li, Xiangcai Huang, Sijia Tian, Rong Hu, Dingxin He, Qiang Gu
2019 2019 9th International Conference on Information Science and Technology (ICIST)  
A novel method named faster randomized matrix completion for latent disease-lncRNA association prediction (FRMCLDA) has been proposed by virtue of improved randomized partial SVD (rSVD-BKI) on a heterogeneous  ...  In this study, a computational prediction model has been remodeled as a matrix completion framework of the recommendation system by completing the unknown items in the rating matrix.  ...  A computational model named SIMCLDA is designed to predict latent disease-lncRNA relationships, taking advantage of the inductive matrix completion (IMC) method (Lu et al., 2018) .  ... 
doi:10.1109/icist.2019.8836825 fatcat:3w5o6uy5vfcp5k4s6eekfgc3tu

Recommending Tumblr Blogs to Follow with Inductive Matrix Completion

Donghyuk Shin, Suleyman Cetintas, Kuang-Chih Lee
2014 ACM Conference on Recommender Systems  
In this paper, we propose a novel inductive matrix completion based blog recommendation method to effectively utilize multiple rich sources of evidence such as the social network and the content as well  ...  Experiments on a large-scale real-world dataset from Tumblr show the effectiveness of the proposed blog recommendation method.  ...  In this paper, we propose a novel inductive matrix completion (IMC) based blog recommendation system that effectively utilizes the social network as well as the rich content and activity (network) data  ... 
dblp:conf/recsys/ShinCL14 fatcat:xc7nsslyuregzoenmotdzsit6i

A review of similarity measures and link prediction models in social networks

S Hemkiran et. al.
2020 International Journal of Computing and Digital Systems  
This study presents a concise review of the similarity measures, techniques employed in predicting future links and application of link prediction with emphasis on dynamic networks.  ...  Social network is a web-based platform which enables people to share information, make new connections and explore various events that occur in society.  ...  To predict the gene-disease associations, the inductive matrix completion method is used [38] .  ... 
doi:10.12785/ijcds/090209 fatcat:xgbiydv5qfhk5i3hwab5v5wv7u

Network representation based on the joint learning of three feature views

Zhonglin Ye, Haixing Zhao, Ke Zhang, Zhaoyang Wang, Yu Zhu
2019 Big Data Mining and Analytics  
Based on Inductive Matrix Completion (IMC), TFNR algorithm introduces the future probabilities between vertices without edges and text features into the procedure of modeling network structures, which  ...  Based on the above two optimization approaches, we propose a novel network representation learning algorithm, Network Representation learning algorithm based on the joint optimization of Three Features  ...  The most popular method of link prediction algorithm is based on matrix factorization.  ... 
doi:10.26599/bdma.2019.9020009 dblp:journals/bigdatama/YeZZWZ19 fatcat:qid2m3mzszetfmyz65mdsdhm5q

An Inductive Logistic Matrix Factorization Model for Predicting Drug-Metabolite Association With Vicus Regularization

Yuanyuan Ma, Lifang Liu, Qianjun Chen, Yingjun Ma
2021 Frontiers in Microbiology  
In this study, we propose a novel algorithm, namely inductive logistic matrix factorization (ILMF) to predict the latent associations between drugs and metabolites.  ...  Moreover, we exploit inductive matrix completion to guide the learning of projection matrices U and V that depend on the low-dimensional feature representation matrices of drugs and metabolites: Fm and  ...  Inspired by the ideas of inductive matrix completion (Jain and Dhillon, 2013; Zeng et al., 2020) and generalized matrix factorization (GMF) (Zhang et al., 2020) , we designed a novel ILMF framework,  ... 
doi:10.3389/fmicb.2021.650366 pmid:33868209 pmcid:PMC8047063 fatcat:ttsff2adu5hpxcli77rdapuvuq

Computational methods and applications for identifying disease-associated lncRNAs as potential biomarkers and therapeutic targets

Congcong Yan, Zicheng Zhang, Siqi Bao, Ping Hou, Meng Zhou, Chongyong Xu, Jie Sun
2020 Molecular Therapy: Nucleic Acids  
Identification of disease-associated lncRNAs is becoming increasingly crucial for fundamentally improving our understanding of molecular mechanisms of disease and developing novel biomarkers and therapeutic  ...  Long non-coding RNAs (lncRNAs) have been recognized as critical components of a broad genomic regulatory network and play pivotal roles in physiological and pathological processes.  ...  to predict lncRNAdisease associations based on the idea of matrix completion.  ... 
doi:10.1016/j.omtn.2020.05.018 pmid:32585624 pmcid:PMC7321789 fatcat:qihynfkmgvhddjndjyc2svuscm

Network approaches for modeling the effect of drugs and diseases

T J Rintala, Arindam Ghosh, V Fortino
2022 Briefings in Bioinformatics  
The network approach is quickly becoming a fundamental building block of computational methods aiming at elucidating the mechanism of action (MoA) and therapeutic effect of drugs.  ...  In this review, we describe network data mining algorithms that are commonly used to study drug's MoA and to improve our understanding of the basis of chronic diseases.  ...  The matrix completion step is very similar to matrix factorization approaches, although predictions for novel DTIs are based on distances between drugs and proteins in the mapped space rather than the  ... 
doi:10.1093/bib/bbac229 pmid:35704883 pmcid:PMC9294412 fatcat:wbmt5bm7mbeazjc56s6fvvap3q

Inductive matrix completion for predicting gene-disease associations

N. Natarajan, I. S. Dhillon
2014 Bioinformatics  
In this paper, we apply a novel matrix completion method called Inductive Matrix Completion to the problem of predicting gene-disease associations; it combines multiple types of evidence (features) for  ...  A crucial advantage of the method is that it is inductive; it can be applied to diseases not seen at training time, unlike traditional matrix completion approaches and network-based inference methods that  ...  CONCLUSIONS In this paper, we have proposed a novel approach based on inductive matrix completion for prioritizing disease genes.  ... 
doi:10.1093/bioinformatics/btu269 pmid:24932006 pmcid:PMC4058925 fatcat:tx7fqdmk25a3jetr2m3d5wtpsq

The NOESIS Network-Oriented Exploration, Simulation, and Induction System [article]

Víctor Martínez, Fernando Berzal, Juan-Carlos Cubero
2017 arXiv   pre-print
detection methods, link scoring, and link prediction, as well as network visualization algorithms.  ...  It also features a complete stand-alone graphical user interface that facilitates the use of all these techniques.  ...  ., 2012) is another influence algorithm based on Katz centrality.  ... 
arXiv:1611.04810v2 fatcat:lw75qtf7xrfyraysu6ivmtuqk4

Predicting Drug-Disease Associations via Multi-Task Learning Based on Collective Matrix Factorization

Feng Huang, Yang Qiu, Qiaojun Li, Shichao Liu, Fuchuan Ni
2020 Frontiers in Bioengineering and Biotechnology  
To this end, we propose a collective matrix factorization-based multi-task learning method (CMFMTL) in this paper.  ...  However, the existing models for predicting drug-disease associations merely concentrate on independent tasks: recommending novel indications to benefit drug repositioning, predicting potential side effects  ...  DRRS (Luo et al., 2018) stated drug repositioning as a recommendation problem and utilized a matrix completion algorithm on a block matrix which was concatenated by a drug-disease association matrix,  ... 
doi:10.3389/fbioe.2020.00218 pmid:32373595 pmcid:PMC7179666 fatcat:7477rkh7vvh5hmylcns2fwysni
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