Demo Data from EDBT 2026 / reviewers in the wild / expert
Changchun Xie
dblp:126/3984
· DBLP profile ↗
2ranked-venue papers
1as first author
1since 2021 · last 2021
0000-0002-1843-4099ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
2 papers |
Bioinformatics and computational biology · 100% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › epigenomics › differential methylation analysis
differentially methylated region detection |
0.5 | 1 | 2021 | ipDMR: identification of differentially methylated regions with interval P-values · Bioinform. 2021 |
Bioinformatics and computational biology › epigenomics › DNA methylation
DNA methylation analysis |
0.5 | 1 | 2021 | ipDMR: identification of differentially methylated regions with interval P-values · Bioinform. 2021 |
Bioinformatics and computational biology
epigenetics |
0.5 | 1 | 2021 | ipDMR: identification of differentially methylated regions with interval P-values · Bioinform. 2021 |
Bioinformatics and computational biology › biostatistics › statistical bioinformatics
statistical genomics |
0.5 | 1 | 2021 | ipDMR: identification of differentially methylated regions with interval P-values · Bioinform. 2021 |
Bioinformatics and computational biology › epigenomics
differential methylation analysis |
0.4 | 1 | 2019 | Differential methylation values in differential methylation analysis · Bioinform. 2019 |
Bioinformatics and computational biology › epigenomics › DNA methylation › DNA methylation analysis
methylation level estimation |
0.4 | 1 | 2019 | Differential methylation values in differential methylation analysis · Bioinform. 2019 |
Methods — techniques the papers use, named apart from their topics
peak detection · 0.5auto-correlated p-values · 0.5linear regression · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | ipDMR: identification of differentially methylated regions with interval P-valuesabstractSUMMARY: ipDMR is an R software tool for identification of differentially methylated regions (DMRs) using auto-correlated P-values for individual CpGs from epigenome-wide association analysis using array or bisulfite sequencing data. It summarizes P-values for adjacent CpGs, identifies association peaks and then extends peaks to find boundaries of DMRs. ipDMR uses BED format files as input and is easy to use. Simulations guided by real data found that ipDMR outperformed current available methods and provided slightly higher true positive rates and much lower false discovery rates. AVAILABILITY AND IMPLEMENTATION: ipDMR is available at https://bioconductor.org/packages/release/bioc/html/ENmix.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Zongli Xu, Changchun Xie, Jack A. Taylor, Liang Niu |
Bioinform. | 2 |
| 2019 | Differential methylation values in differential methylation analysisabstractMOTIVATION: Both β-value and M-value have been used as metrics to measure methylation levels. The M-value is more statistically valid for the differential analysis of methylation levels. However, the β-value is much more biologically interpretable and needs to be reported when M-value method is used for conducting differential methylation analysis. There is an urgent need to know how to interpret the degree of differential methylation from the M-value. In M-value linear regression model, differential methylation M-value ΔM can be easily obtained from the coefficient estimate, but it is not straightforward to get the differential methylation β-value, Δβ since it cannot be obtained from the coefficient alone. RESULTS: To fill the gap, we have built a bridge to connect the statistically sound M-value linear regression model and the biologically interpretable Δβ. In this article, three methods were proposed to calculate differential methylation values, Δβ from M-value linear regression model and compared with the Δβ directly obtained from β-value linear regression model. We showed that under the condition that M-value linear regression model is correct, the method M-model-coef is the best among the four methods. M-model-M-mean method works very well too. If the coefficients α0, α2,…αp are not given (as 'MethLAB' package), the M-model-M-mean method should be used. The Δβ directly obtained from β-value linear regression model can give very biased results, especially when M-values are not in (-2, 2) or β-values are not in (0.2, 0.8). AVAILABILITY AND IMPLEMENTATION: The dataset for example is available at the National Center for Biotechnology Information Gene Expression Omnibus repository, GSE104778. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Changchun Xie, Yuet-Kin Leung, Aimin Chen, Ding-Xin Long, Catherine Hoyo, Shuk-Mei Ho |
Bioinform. | 1 |