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Kaikun Xu

dblp:228/0313 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2019
0000-0002-3979-8780ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2

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 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
proteomics
0.722019
PANDA: A comprehensive and flexible tool for quantitative proteomics data analysis · Bioinform. 2019
PANDA-view: an easy-to-use tool for statistical analysis and visualization of quantitative proteomics data · Bioinform. 2018
Bioinformatics and computational biology › proteomics
quantitative proteomics
0.722019
PANDA: A comprehensive and flexible tool for quantitative proteomics data analysis · Bioinform. 2019
PANDA-view: an easy-to-use tool for statistical analysis and visualization of quantitative proteomics data · Bioinform. 2018
Bioinformatics and computational biology › omics data analysis
omics data visualization
0.112018
PANDA-view: an easy-to-use tool for statistical analysis and visualization of quantitative proteomics data · Bioinform. 2018

Methods — techniques the papers use, named apart from their topics

labeled quantification · 0.4label-free quantification · 0.4statistical test · 0.3principal component analysis · 0.3normalization · 0.3missing value imputation · 0.3clustering · 0.3
YearPublicationVenuePosition
2019 PANDA: A comprehensive and flexible tool for quantitative proteomics data analysis
abstract
SUMMARY: As the experiment techniques and strategies in quantitative proteomics are improving rapidly, the corresponding algorithms and tools for protein quantification with high accuracy and precision are continuously required to be proposed. Here, we present a comprehensive and flexible tool named PANDA for proteomics data quantification. PANDA, which supports both label-free and labeled quantifications, is compatible with existing peptide identification tools and pipelines with considerable flexibility. Compared with MaxQuant on several complex datasets, PANDA was proved to be more accurate and precise with less computation time. Additionally, PANDA is an easy-to-use desktop application tool with user-friendly interfaces. AVAILABILITY AND IMPLEMENTATION: PANDA is freely available for download at https://sourceforge.net/projects/panda-tools/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Mansheng Li, Chaoping Guo, Yuqing Ding, Kaikun Xu, Mingfei Han 0001, Fuchu He
Bioinform.5
2018 PANDA-view: an easy-to-use tool for statistical analysis and visualization of quantitative proteomics data
abstract
Summary: Compared with the numerous software tools developed for identification and quantification of -omics data, there remains a lack of suitable tools for both downstream analysis and data visualization. To help researchers better understand the biological meanings in their -omics data, we present an easy-to-use tool, named PANDA-view, for both statistical analysis and visualization of quantitative proteomics data and other -omics data. PANDA-view contains various kinds of analysis methods such as normalization, missing value imputation, statistical tests, clustering and principal component analysis, as well as the most commonly-used data visualization methods including an interactive volcano plot. Additionally, it provides user-friendly interfaces for protein-peptide-spectrum representation of the quantitative proteomics data. Availability and implementation: PANDA-view is freely available at https://sourceforge.net/projects/panda-view/. Supplementary information: Supplementary data are available at Bioinformatics online.
Kaikun Xu, Chaoping Guo, Fuchu He
Bioinform.2