ArticleBMC bioinformatics2020
REW-ISA: unveiling local functional blocks in epi-transcriptome profiling data via an RNA expression-weighted iterative signature algorithm.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A novel biclustering algorithm for mining m6A co-methylation patterns based on beta-binomial distribution and data screening strategy.PLoS computational biology · 2026Article
- REW-ISA V2: A Biclustering Method Fusing Homologous Information for Analyzing and Mining Epi-Transcriptome Data.Frontiers in genetics · 2021Article
- Recent advances in functional annotation and prediction of the epitranscriptome.Computational and structural biotechnology journal · 2021Review
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Authors and funding
5 authors.
Funding
Abstract
backgroundRecent studies have shown that N
resultsDue to the high economic and time cost of wet experimental methods, revealing methylation patterns through computational models has become a more preferable way, and drawn more and more attention. Considering the theoretical basics and applications of conventional clustering methods, an RNA Expression Weighted Iterative Signature Algorithm (REW-ISA) is proposed to find potential local functional blocks (LFBs) based on MeRIP-Seq data, where sites are hyper-methylated or hypo-methylated simultaneously across the specific conditions. REW-ISA adopts RNA expression levels of each site as weights to make sites of lower expression level less significant. It starts from random sets of sites, then follows iterative search strategies by thresholds of rows and columns to find the LFBs in m
conclusionsREW-ISA finds potential local functional patterns presented in m
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Registered trials
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