Pathway-disease Association Prediction Based on Graph Regularized Logistic Matrix Factorization (PDA-GRLMF)
DOI:
https://doi.org/10.21015/vtcs.v10i1.1259Abstract
Complex alterations to the cellular machinery occur as a result of diseases. There are distinctive patterns associated with a disease in the gene expression profile of the affected cells. As a result, these profiles can be used to extract additional biological information about an illness, which helps us better identify and evaluate disease risks. Human pathway-disease interaction research is a recurrent area of interest for the biomedical community. Finding the processes or connections between diseases and pathways can be aided by this association. This paper provides an overview of human pathway and human disease, with the accuracy of disease identification has been less than satisfactory. In predicting disease-pathway interactions, this study suggests a computer model. In this research study we proposed the Graph Regularized Logistic MatrixFactorization (GRLMF) method for pathway-disease association prediction. A cutting-edge computational model called the PDA-GRLMF disease-pathway association
model can predict probable pathway-disease associations. The model can also assist pathologists in comprehending the relationships between diseasepathway linkages, therapies, and outcomes. In order to increase the association
between disease variation and new molecular correlations between genetic mutations, we carried out a pathway-based investigation. On the basis of shared gene interactions among pathways-disease, we created a biological network, and then we used network analysis to try and understand how a disease constructed the pathway-pathway network and then disease-disease network. To merge the gathered biological data, which was based on the pair similarity of sequence expression weights, we employed the heterogeneous network of pathway-disease relationships. The ROC (AUC) score achieved for the best prediction results was 0.8018%, and the precision-recall curve had two classes. These findings suggest that our strategy outperforms previously suggested methods in terms of scientific performance. By contrasting them with established connections and conducting a literature search, we projected relationships between pathogen, DD, and disease-pathway.
References
L. Eronen and H. Toivonen, “Biomine: predicting links between biological entities using network models
of heterogeneous databases,” BMC Bioinformatics, vol. 13, no. 1, p. 119, 2012.
T. Gaudelet, N. Malod-Dognin, J. Sanchez-Valle, V. Pancaldi, A. Valencia, and N. Przulj, “Unveiling new
disease, pathway, and gene associations via multi-scale neural networks,” arXiv [q-bio.QM], 2019.
M. Agrawal, M. Zitnik, and J. Leskovec, “Large-scale analysis of disease pathways in the human interactome,”
in Biocomputing 2018, 2018.
H. Lee and M. Shin, “Mining pathway associations for disease-related pathway activity analysis based
on gene expression and methylation data,” BioData Min., vol. 10, no. 1, p. 3, 2017.
R. Giugno, A. Pulvirenti, L. Cascione, G. Pigola, and A. Ferro, “MIDClass: Microarray data classification
by association rules and gene expression intervals,” PLoS One, vol. 8, no. 8, p. e69873, 2013.
F. Emmert-Streib and G. V. Glazko, “Pathway analysis of expression data: deciphering functional building
blocks of complex diseases,” PLoS Comput. Biol., vol. 7, no. 5, p. e1002053, 2011.
Y. Liu and Z. Hu, “Identification of collaborative driver pathways in breast cancer,” BMC Genomics, vol.
, p. 605, 2014.
M. List, N. Alcaraz, and R. Batra, “De Novo pathway-based classification of breast cancer subtypes,”
Methods Mol. Biol., vol. 2074, pp. 201–213, 2020.
E. Lee, H.-Y. Chuang, J.-W. Kim, T. Ideker, and D. Lee, “Inferring pathway activity toward precise disease
classification,” PLoS Comput. Biol., vol. 4, no. 11, p. e1000217, 2008.
P. Khatri, M. Sirota, and A. J. Butte, “Ten years of pathway analysis: current approaches and outstanding
challenges,” PLoS Comput. Biol., vol. 8, no. 2, p. e1002375, 2012.
Y. Liu and M. R. Chance, “Pathway analyses and understanding disease associations,” Curr. Genet.
Med. Rep., vol. 1, no. 4, pp. 230–238, 2013.
D. Yu, M. Kim, G. Xiao, and T. H. Hwang, “Review of biological network data and its applications,”
Genomics Inform., vol. 11, no. 4, pp. 200–210, 2013.
Y. Li and P. Agarwal, “A pathway-based view of human diseases and disease relationships,” PLoS One,
vol. 4, no. 2, p. e4346, 2009.
K. Wang, M. Li, and M. Bucan, “Pathway-based approaches for analysis of genomewide association
studies,” Am. J. Hum. Genet., vol. 81, no. 6, pp. 1278–1283, 2007.
S. Köhler, S. Bauer, D. Horn, and P. N. Robinson, “Walking the interactome for prioritization of candidate
disease genes,” Am. J. Hum. Genet., vol. 82, no. 4, pp. 949–958, 2008.
Y. Li and J. C. Patra, “Integration of multiple data sources to prioritize candidate genes using discounted
rating system,” BMC Bioinformatics, vol. 11 Suppl 1, no. S1, p. S20, 2010.
Z.-C. Zhang, X.-F. Zhang, M. Wu, L. Ou-Yang, X.-M. Zhao, and X.-L. Li, “A graph regularized generalized
matrix factorization model for predicting links in biomedical bipartite networks,” Bioinformatics, vol.
, no. 11, pp. 3474–3481, 2020.
Z. Gao, Y.-T. Wang, Q.-W. Wu, J.-C. Ni, and C.-H. Zheng, “Graph regularized L2,1-nonnegative matrix
factorization for miRNA-disease association prediction,” BMC Bioinformatics, vol. 21, no. 1, p. 61, 2020.
C. Wu, R. Gao, D. Zhang, S. Han, and Y. Zhang, “PRWHMDA: Human microbe-disease association prediction
by random walk on the heterogeneous network with PSO,” Int. J. Biol. Sci., vol. 14, no. 8, pp.
–857, 2018.
X. Shen, Y. Chen, X. Jiang, X. Hu, T. He, and J. Yang, “Predicting disease-microbe association by random
walking on the heterogeneous network,” in 2016 IEEE International Conference on Bioinformatics and
Biomedicine (BIBM), 2016.
S. Zou, J. Zhang, and Z. Zhang, “A novel approach for predicting microbe-disease associations by birandom
walk on the heterogeneous network,” PLoS One, vol. 12, no. 9, p. e0184394, 2017.
A. Ghulam, X. Lei, M. Guo, and C. Bian, “Disease-pathway association prediction based on random
walks with restart and PageRank,” IEEE Access, vol. 8, pp. 72021–72038, 2020.
H. N. Kim et al., “Effectiveness of direct-acting antiviral therapy in patients with human immunodeficiency
virus-hepatitis C virus coinfection in routine clinical care: A multicenter study,” Open Forum
Infect. Dis., vol. 6, no. 4, p. ofz100, 2019.
S. N. Lewis, E. Nsoesie, C. Weeks, D. Qiao, and L. Zhang, “Prediction of disease and phenotype associations
from genome-wide association studies,” PLoS One, vol. 6, no. 11, p. e27175, 2011.
J.-A. R. Sarmiento, A. Lao, and G. A. Solano, “Pathway-based human disease clustering tool using
self-organizing maps,” in 2017 8th International Conference on Information, Intelligence, Systems Applications
(IISA), 2017.
K. Jiang, Y. Huang, and J. Robertson, “A method of biological pathway similarity search using high
performance computing,” Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., vol. 2009, pp. 4933–4936, 2009.
X. Chen, B. Ren, M. Chen, Q. Wang, L. Zhang, and G. Yan, “NLLSS: Predicting synergistic drug combinations
based on semi-supervised learning,” PLoS Comput. Biol., vol. 12, no. 7, p. e1004975, 2016.
X. Chen, C. C. Yan, X. Zhang, and Z.-H. You, “Long non-coding RNAs and complex diseases: from
experimental results to computational models,” Brief. Bioinform., p. bbw060, 2016.
I. Baskara-Yhuellou and J. Tost, “The impact of microRNAs on alterations of gene regulatory networks
in allergic diseases,” Adv. Protein Chem. Struct. Biol., vol. 120, pp. 237–312, 2020.
L. Hood, J. R. Heath, M. E. Phelps, and B. Lin, “Systems biology and new technologies enable predictive
and preventative medicine,” Science, vol. 306, no. 5696, pp. 640–643, 2004.
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