Elucidating Genome-Wide Protein-RNA Interactions Using Differential Evolution
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 272-282 |
Journal / Publication | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
Volume | 16 |
Issue number | 1 |
Online published | 22 Nov 2017 |
Publication status | Published - Jan 2019 |
Link(s)
Abstract
RNA-binding proteins (RBPs) play an important role in the post-transcriptional control of RNAs, such as splicing, polyadenylation, mRNA stabilization, mRNA localization, and translation. Thanks to the recent breakthrough, non-negative matrix factorization (NMF) has been developed to combine multiple data sources to discover non-overlapping and class-specific RNA binding patterns. However, several challenges still exist in determining the number of latent dimensions in the factorization steps. In most circumstances, it is often assumed that the number of latent dimensions (or components) is given. Such trial-and-error procedures can be tedious in practice. In order to address this problem, differential evolution algorithm is proposed as the model selection method to choose the suitable number of ranks via differential evolution, which can adaptively decompose the input protein-RNA data matrix into different nonnegative components. Experimental results demonstrate that the proposed algorithms can improve the factorization quality over the recent state-of-the-arts. The effectiveness of the proposed algorithms are supported by comprehensive performance benchmarking on 31 genome-wide cross-linking immunoprecipitation (CLIP) coupled with high-throughput sequencing (CLIP-seq) datasets. In addition, time complexity analysis and parameter analysis are conducted to demonstrate the robustness of the proposed methods.
Research Area(s)
- Bioinformatics, CLIP-seq datasets, Genomics, Optimization, population based optimization algorithm, Proteins, RNA, RNA binding proteins, Sociology, Statistics
Citation Format(s)
Elucidating Genome-Wide Protein-RNA Interactions Using Differential Evolution. / Li, Xiangtao; Wong, Ka-Chun.
In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 16, No. 1, 01.2019, p. 272-282.
In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 16, No. 1, 01.2019, p. 272-282.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review