EDBT 2026 Demo / reviewers in the wild / expert
Yi-Ching Wang
dblp:152/7621
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2016
0000-0002-7694-2067ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1Applied, interdisciplinary, general and emerging computing · 1
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
1 paper |
Bioinformatics and computational biology · 87% Medical and health informatics · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › epigenomics
epigenetic biomarker discovery |
0.2 | 1 | 2014 | Network-based analysis identifies epigenetic biomarkers of esophageal squamous cell carcinoma progression · Bioinform. 2014 |
Bioinformatics and computational biology › gene regulation › gene regulatory network
gene regulatory network analysis |
0.2 | 1 | 2014 | Network-based analysis identifies epigenetic biomarkers of esophageal squamous cell carcinoma progression · Bioinform. 2014 |
Medical and health informatics
cancer prognosis |
0.1 | 1 | 2014 | Network-based analysis identifies epigenetic biomarkers of esophageal squamous cell carcinoma progression · Bioinform. 2014 |
Methods — techniques the papers use, named apart from their topics
survival analysis · 0.2network topology analysis · 0.2differential methylation analysis · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Autonomic Smart Home Operations Management Using CWMP: A Task-Centric View
Chun-Feng Liao, Shih-Ting Huang, Yi-Ching Wang |
IEA/AIE | 3 |
| 2014 | Network-based analysis identifies epigenetic biomarkers of esophageal squamous cell carcinoma progressionabstractMOTIVATION: A rapid progression of esophageal squamous cell carcinoma (ESCC) causes a high mortality rate because of the propensity for metastasis driven by genetic and epigenetic alterations. The identification of prognostic biomarkers would help prevent or control metastatic progression. Expression analyses have been used to find such markers, but do not always validate in separate cohorts. Epigenetic marks, such as DNA methylation, are a potential source of more reliable and stable biomarkers. Importantly, the integration of both expression and epigenetic alterations is more likely to identify relevant biomarkers. RESULTS: We present a new analysis framework, using ESCC progression-associated gene regulatory network (GRN escc), to identify differentially methylated CpG sites prognostic of ESCC progression. From the CpG loci differentially methylated in 50 tumor-normal pairs, we selected 44 CpG loci most highly associated with survival and located in the promoters of genes more likely to belong to GRN escc. Using an independent ESCC cohort, we confirmed that 8/10 of CpG loci in the promoter of GRN escc genes significantly correlated with patient survival. In contrast, 0/10 CpG loci in the promoter genes outside the GRN escc were correlated with patient survival. We further characterized the GRN escc network topology and observed that the genes with methylated CpG loci associated with survival deviated from the center of mass and were less likely to be hubs in the GRN escc. We postulate that our analysis framework improves the identification of bona fide prognostic biomarkers from DNA methylation studies, especially with partial genome coverage. Chun-Pei Cheng, I-Ying Kuo, Hakan Alakus, Kelly A. Frazer, Olivier Harismendy, Yi-Ching Wang, Vincent S. Tseng |
Bioinform. | 6 |