Demo Data from EDBT 2026 / reviewers in the wild / expert
Gang Peng 0004
dblp:84/1415-4
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2019
0000-0003-0307-9351ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author
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 · 100% |
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 › proteomics › post-translational modification
post-translational modification analysis |
0.4 | 1 | 2019 | ProteomicsBrowser: MS/proteomics data visualization and investigation · Bioinform. 2019 |
Bioinformatics and computational biology
proteomics |
0.4 | 1 | 2019 | ProteomicsBrowser: MS/proteomics data visualization and investigation · Bioinform. 2019 |
Bioinformatics and computational biology › proteomics › computational proteomics
proteomics data visualization |
0.4 | 1 | 2019 | ProteomicsBrowser: MS/proteomics data visualization and investigation · Bioinform. 2019 |
Methods — techniques the papers use, named apart from their topics
statistical analysis · 0.4peptide alignment · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | ProteomicsBrowser: MS/proteomics data visualization and investigationabstractSUMMARY: Large-scale, quantitative proteomics data are being generated at ever increasing rates by high-throughput, mass spectrometry technologies. However, due to the complexity of these large datasets as well as the increasing numbers of post-translational modifications (PTMs) that are being identified, developing effective methods for proteomic visualization has been challenging. ProteomicsBrowser was designed to meet this need for comprehensive data visualization. Using peptide information files exported from mass spectrometry search engines or quantitative tools as input, the peptide sequences are aligned to an internal protein database such as UniProtKB. Each identified peptide ion including those with PTMs is then visualized along the parent protein in the Browser. A unique property of ProteomicsBrowser is the ability to combine overlapping peptides in different ways to focus analysis of sequence coverage, charge state or PTMs. ProteomicsBrowser includes other useful functions, such as a data filtering tool and basic statistical analyses to qualify quantitative data. AVAILABILITY AND IMPLEMENTATION: ProteomicsBrowser is implemented in Java8 and is available at https://medicine.yale.edu/keck/nida/proteomicsbrowser.aspx and https://github.com/peng-gang/ProteomicsBrowser. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Gang Peng 0004, Rashaun Wilson, Yishuo Tang, TuKiet T. Lam, Angus C. Nairn, Kenneth R. Williams, Hongyu Zhao 0003 |
Bioinform. | 1 |