Demo Data from EDBT 2026 / reviewers in the wild / expert
Jong Hwan Kim
dblp:154/0412
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, 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 · 56% Medical and health informatics · 44% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Medical and health informatics › biomedical data science
cancer informatics |
0.2 | 1 | 2014 | APPEX: analysis platform for the identification of prognostic gene expression signatures in cancer · Bioinform. 2014 |
Bioinformatics and computational biology › gene expression analysis › molecular signature analysis
prognostic gene signature identification |
0.2 | 1 | 2014 | APPEX: analysis platform for the identification of prognostic gene expression signatures in cancer · Bioinform. 2014 |
Bioinformatics and computational biology
survival analysis |
0.1 | 1 | 2014 | APPEX: analysis platform for the identification of prognostic gene expression signatures in cancer · Bioinform. 2014 |
Methods — techniques the papers use, named apart from their topics
time-dependent ROC · 0.2supervised principal components · 0.2cox regression · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | APPEX: analysis platform for the identification of prognostic gene expression signatures in cancerabstractSUMMARY: Because cancer has heterogeneous clinical behaviors due to the progressive accumulation of multiple genetic and epigenetic alterations, the identification of robust molecular signatures for predicting cancer outcome is profoundly important. Here, we introduce the APPEX Web-based analysis platform as a versatile tool for identifying prognostic molecular signatures that predict cancer diversity. We incorporated most of statistical methods for survival analysis and implemented seven survival analysis workflows, including CoxSingle, CoxMulti, IntransSingle, IntransMulti, SuperPC, TimeRoc and multivariate. A total of 236 publicly available datasets were collected, processed and stored to support easy independent validation of prognostic signatures. Two case studies including disease recurrence and bladder cancer progression were described using different combinations of the seven workflows. AVAILABILITY AND IMPLEMENTATION: APPEX is freely available at http://www.appex.kr. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Seon-Kyu Kim, Jong Hwan Kim, Seok-Joong Yun, Wun-Jae Kim, Seon-Young Kim |
Bioinform. | 2 |