Review on Integration Analysis and Application of Multi-omics Data

被引:3
作者
Zhong, Yating [1 ]
Lin, Yanmei [1 ]
Chen, Dingjia [1 ]
Peng, Yuzhong [1 ]
Zeng, Yuanpeng [1 ]
机构
[1] Key Laboratory of Scientific Computing and Intelligent Information Processing of Guangxi Universities, College of Computer and Information Engineering, Nanning Normal University, Nanning
关键词
biological information; data integration; omics data analysis; multi-omics data;
D O I
10.3778/j.issn.1002-8331.2106-0341
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
With the continuous emergence and popularization of new omics sequencing technology, a large number of omics data have been produced, which is of great significance for people to further study and reveal the mysteries of life. Using multi-omics data to integrate and analyze life science problems can obtain more abundant and more comprehensive information related to life system, which has become a new direction for scientists to explore the mechanism of life. This paper introduces the research background and significance of multi- omics data integration analysis, summarizes the methods of data integration analysis of multiomics in recent years and the applied research in related fields, and finally discusses the current existing problems and future prospects of multi-omics data integration analysis methods. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.
引用
收藏
页码:1 / 17
页数:16
相关论文
共 106 条
[1]  
WANG X C, YANG Z R,, WANG M., Et al., High-throughput sequencing technology and its applications[J], Chinese Journal of Bioengineering, 32, 1, pp. 109-114, (2012)
[2]  
LONG Z P, WANG F., Study design and statistical methods used for integrative analysis on multi-omics in cancer epidemiology[J], Chinese Journal of Epidemiology, 5, pp. 788-793, (2020)
[3]  
ILIYAN M, MACIEJ K, MILKO K, Et al., A novel framework for horizontal and vertical data integration in cancer studies with application to survival time prediction models[J], Biology Direct, 14, 3, (2019)
[4]  
ULFENBORG B., Vertical and horizontal integration of multi- omics data with miodin[J], BMC Bioinformatics, 20, 1, (2019)
[5]  
MARINKA Z, FRANCIS N, WANG B, Et al., Machine learning for integrating data in biology and medicine principles,practice,and opportunities[J], Information Fusion, 50, pp. 71-91, (2019)
[6]  
NIMROD R, RON S., Multi-omic and multi-view clustering algorithms:review and cancer benchmark[J], Narnia, 47, 2, (2019)
[7]  
KIM D, JOUNG J G, SOHN K A, Et al., Knowledge boosting:a graph-based integration approach with multiomics data and genomic knowledge for cancer clinical outcome prediction[J], Journal of the American Medical Informatics Association, 1, pp. 109-120, (2015)
[8]  
EBLEN J D,, GERLING I C,, SATON A M, Et al., Graph algorithms for integrated biological analysis,with applications to type 1 diabetes data[M], Clustering challenges in biological networks, pp. 219-220, (2009)
[9]  
JIANG H, DENG Y, CHEN H S, Et al., Joint analysis of two microarray gene- expression data sets to select lung adenocarcinoma marker genes[J], BMC Bioinformatics, 5, 1, (2004)
[10]  
ARGELAGUET R, VELTEN B, ARNOL D, Et al., Multiomics factor analysis—a framework for unsupervised integration of multi-omics data sets[J], Molecular Systems Biology, 14, 6, (2018)