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Jong Hwan Kim

dblp:154/0412 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Medical and health informatics › biomedical data science
cancer informatics
0.212014
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
0.212014
APPEX: analysis platform for the identification of prognostic gene expression signatures in cancer · Bioinform. 2014
Bioinformatics and computational biology
survival analysis
0.112014
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
YearPublicationVenuePosition
2014 APPEX: analysis platform for the identification of prognostic gene expression signatures in cancer
abstract
SUMMARY: 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