A Novel Method to Predict Essential Proteins Based on Diffusion Distance Networks

Zhao, Bihai; Han, Xiao; Liu, Xiner; Luo, Yingchun; Hu, Sai; Wang, Lei

doi:10.1109/access.2020.2972922

Cited by 12 publications

(12 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the contrary, it is convenient and fast to predict their associations by computation methods. Recently, the application of computation methods in bioinformatics has attracted wide attention, such as identifying lncRNA and mRNA Co-expression Modules [14], calculating miRNA functional similarity [15], the discovery of miRNA-mRNA regulatory modules [16] [17], Predict Essential Proteins [18] [19], and the prediction of miRNAtarget interactions [20]. Before 2013, there was no computation methods to predict lncRNA-disease associations.…”

Section: Introductionmentioning

confidence: 99%

LncRNA-Disease Associations Prediction Based on Neural Network-Based Matrix Factorization

Liu

Zhang

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Numerous experiments have demonstrated that long non-coding RNA (lncRNA) play an important role in various systems of the human body. LncRNA deletions or mutations can cause human disease. The prediction of lncRNA-disease associations is conducive to the diagnosis and prevention of complex diseases. As we all know, it is a time-consuming and expensive process to predict lncRNA-disease associations via biological experiments. However, the computation methods can effectively discover lncRNA-disease associations with less human and material resources. In this paper, we propose a neural network-based matrix factorization model to predict lncRNA-disease associations, which is called NeuMFLDA. NeuMFLDA first converts the one-hot encoding of disease or lncRNA into word vector via the embedding layer. Then combined with the memorization of the conventional matrix factorization and the generalization of the multi-layer perceptron, the lncRNA-disease associations can be predicted more accurately. In addition, as opposed to conventional pointwise loss function, a new pairwise loss function is proposed to update our model parameters. Our new loss function optimizes the model from the perspective of ranking priority, which is more in line with the solution to the lncRNA-disease associations prediction task. Experiments show that NeuMFLDA reaches average AUCs of 0.904 ± 0.003 and 0.918 ± 0.002 in the framework of 5-fold cross validation and Leave-one-out cross validation, which is superior to three the-stateof-art methods. In case studies, 9, 9 and 8 out of top-10 candidate lncRNAs are verified by recently published literatures for hepatocelluar carcinoma, kidney cancer and ovarian cancer, respectively. In short, NeuMFLDA is an effective tool for predicting lncRNA-disease associations. INDEX TERMS LncRNA-disease associations, embedding, neural network, pairwise loss function.

show abstract

Section: Introductionmentioning

confidence: 99%

LncRNA-Disease Associations Prediction Based on Neural Network-Based Matrix Factorization

Liu

Zhang

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…There are various guises of network propagation, such as random walks on graphs [20], the Google PageRank search algorithm [21], heat diffusion processes [22], graph kernels [23], etc. In biological network, plenty of methods based on network propagation have been widely applied to essential proteins identification [24,25], drug synergy prediction [26], tumors classification [27], disease associated genes identification [28,29], microbedisease associations inference [30] and protein functions prediction [31], which demonstrated that network propagation is a powerful data transformation method of broad utility in genetic research [18]. Additionally, the rationality of combining the protein domain, complex information and PINs for functions prediction is substantiated by the DCS and DSCP methods.…”

Section: Introductionmentioning

confidence: 99%

NPF:network propagation for protein function prediction

Zhao

Jiang

et al. 2020

BMC Bioinformatics

Self Cite

View full text Add to dashboard Cite

Background: The accurate annotation of protein functions is of great significance in elucidating the phenomena of life, treating disease and developing new medicines. Various methods have been developed to facilitate the prediction of these functions by combining protein interaction networks (PINs) with multi-omics data. However, it is still challenging to make full use of multiple biological to improve the performance of functions annotation. Results: We presented NPF (Network Propagation for Functions prediction), an integrative protein function predicting framework assisted by network propagation and functional module detection, for discovering interacting partners with similar functions to target proteins. NPF leverages knowledge of the protein interaction network architecture and multi-omics data, such as domain annotation and protein complex information, to augment protein-protein functional similarity in a propagation manner. We have verified the great potential of NPF for accurately inferring protein functions. According to the comprehensive evaluation of NPF, it delivered a better performance than other competing methods in terms of leaveone-out cross-validation and tenfold cross validation. Conclusions: We demonstrated that network propagation, together with multiomics data, can both discover more partners with similar function, and is unconstricted by the "small-world" feature of protein interaction networks. We conclude that the performance of function prediction depends greatly on whether we can extract and exploit proper functional information of similarity from protein correlations.

show abstract

“…There are various guises of network propagation, such as random walks on graphs [20], the Google PageRank search algorithm [21], heat diffusion processes [22], graph kernels [23], etc. In biological network, plenty of methods based on network propagation have been widely applied to essential proteins identification [24,25], drug synergy prediction [26], tumors classification [27], disease associated genes identification [28,29], microbe-disease associations inference [30] and protein functions prediction [31], which demonstrated that network propagation is a powerful data transformation method of broad utility in genetic research [18]. Additionally, the rationality of combining the protein domain, complex information and PINs for functions prediction is substantiated by the DCS and DSCP methods.…”

Section: Introductionmentioning

confidence: 99%

NPF: Network propagation for protein function prediction

Zhao

Zhi-hong

Jiang³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Background: The accurate annotation of protein functions is of great significance in elucidating the phenomena of life, treating disease and developing new medicines. Various methods have been developed to facilitate the prediction of these functions by combining protein interaction networks (PINs) with multi-omics data. However, it is still challenging to make full use of multiple biological to improve the performance of functions annotation.Results: We presented NPF (Network Propagation for Functions prediction), an integrative protein function predicting framework assisted by network propagation and functional module detection, for discovering interacting partners with similar functions to target proteins. NPF leverages knowledge of the protein interaction network architecture and multi-omics data, such as domain annotation and protein complex information, to augment protein-protein functional similarity in a propagation manner. We have verified the great potential of NPF for accurately inferring protein functions. According to the comprehensive evaluation of NPF, it delivered a better performance than other competing methods in terms of leave-one-out cross-validation and ten-fold cross validation.Conclusions: We demonstrated that network propagation, together with multi-omics data, can both discover more partners with similar function, and is unconstricted by the “small-world” feature of protein interaction networks. We conclude that the performance of function prediction depends greatly on whether we can extract and exploit proper functional information of similarity from protein correlations.

show abstract

A Novel Method to Predict Essential Proteins Based on Diffusion Distance Networks

Cited by 12 publications

References 39 publications

LncRNA-Disease Associations Prediction Based on Neural Network-Based Matrix Factorization

LncRNA-Disease Associations Prediction Based on Neural Network-Based Matrix Factorization

NPF:network propagation for protein function prediction

NPF: Network propagation for protein function prediction

Contact Info

Product

Resources

About