Matrix factorization for biomedical link prediction and scRNA-seq data imputation: an empirical survey

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Matrix factorization for biomedical link prediction and scRNA-seq data imputation: an empirical survey
Title:
Matrix factorization for biomedical link prediction and scRNA-seq data imputation: an empirical survey
Other Titles:
Briefings in Bioinformatics
Publication Date:
01 December 2021
Citation:
Ou-Yang, L., Lu, F., Zhang, Z.-C., & Wu, M. (2021). Matrix factorization for biomedical link prediction and scRNA-seq data imputation: an empirical survey. Briefings in Bioinformatics. doi:10.1093/bib/bbab479
Abstract:
Abstract Advances in high-throughput experimental technologies promote the accumulation of vast number of biomedical data. Biomedical link prediction and single-cell RNA-sequencing (scRNA-seq) data imputation are two essential tasks in biomedical data analyses, which can facilitate various downstream studies and gain insights into the mechanisms of complex diseases. Both tasks can be transformed into matrix completion problems. For a variety of matrix completion tasks, matrix factorization has shown promising performance. However, the sparseness and high dimensionality of biomedical networks and scRNA-seq data have raised new challenges. To resolve these issues, various matrix factorization methods have emerged recently. In this paper, we present a comprehensive review on such matrix factorization methods and their usage in biomedical link prediction and scRNA-seq data imputation. Moreover, we select representative matrix factorization methods and conduct a systematic empirical comparison on 15 real data sets to evaluate their performance under different scenarios. By summarizing the experimental results, we provide general guidelines for selecting matrix factorization methods for different biomedical matrix completion tasks and point out some future directions to further improve the performance for biomedical link prediction and scRNA-seq data imputation.
License type:
Publisher Copyright
Funding Info:
This work is supported in part by funds from the National Natural Science Foundation of China [61602309, 11871026, 11871237], Guangdong Basic and Applied Basic Research Foundation [2019A1515011384], Shenzhen Fundamental Research Program [JCYJ20170817095210760]
Description:
This is a pre-copyedited, author-produced version of an article accepted for publication in Briefings in Bioinformatics following peer review. The version of recordOu-Yang, L., Lu, F., Zhang, Z.-C., & Wu, M. (2021). Matrix factorization for biomedical link prediction and scRNA-seq data imputation: an empirical survey. Briefings in Bioinformatics. doi:10.1093/bib/bbab479 is available online at https://doi.org/10.1093/bib/bbab479
ISSN:
1477-4054
1467-5463
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