Keyi Ren

dblp:370/4844 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none

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

Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

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%
Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › gene expression analysis
differential expression analysis
0.712023
Visual Omics: a web-based platform for omics data analysis and visualization with rich graph-tuning capabilities · Bioinform. 2023
Bioinformatics and computational biology › functional genomics
functional enrichment analysis
0.712023
Visual Omics: a web-based platform for omics data analysis and visualization with rich graph-tuning capabilities · Bioinform. 2023
Bioinformatics and computational biology
omics data analysis
0.712023
Visual Omics: a web-based platform for omics data analysis and visualization with rich graph-tuning capabilities · Bioinform. 2023
Bioinformatics and computational biology › protein analysis › protein-protein interaction
protein-protein interaction analysis
0.712023
Visual Omics: a web-based platform for omics data analysis and visualization with rich graph-tuning capabilities · Bioinform. 2023
Bioinformatics and computational biology
transcriptomics
0.712023
Visual Omics: a web-based platform for omics data analysis and visualization with rich graph-tuning capabilities · Bioinform. 2023
Visualization and visual analytics
scientific visualization
0.212023
Visual Omics: a web-based platform for omics data analysis and visualization with rich graph-tuning capabilities · Bioinform. 2023

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

dimensionality reduction · 1.3clustering · 1.3
YearPublicationVenuePosition
2023 Visual Omics: a web-based platform for omics data analysis and visualization with rich graph-tuning capabilities
abstract
SUMMARY: With the continuous development of high-throughput sequencing technology, bioinformatic analysis of omics data plays an increasingly important role in life science research. Many R packages are widely used for omics analysis, such as DESeq2, clusterProfiler and STRINGdb. And some online tools based on them have been developed to free bench scientists from programming with these R packages. However, the charts generated by these tools are usually in a fixed, non-editable format and often fail to clearly demonstrate the details the researchers intend to express. To address these issues, we have created Visual Omics, an online tool for omics data analysis and scientific chart editing. Visual Omics integrates multiple omics analyses which include differential expression analysis, enrichment analysis, protein domain prediction and protein-protein interaction analysis with extensive graph presentations. It can also independently plot and customize basic charts that are involved in omics analysis, such as various PCA/PCoA plots, bar plots, box plots, heat maps, set intersection diagrams, bubble charts and volcano plots. A distinguishing feature of Visual Omics is that it allows users to perform one-stop omics data analyses without programming, iteratively explore the form and layout of graphs online and fine-tune parameters to generate charts that meet publication requirements. AVAILABILITY AND IMPLEMENTATION: Visual Omics can be used at http://bioinfo.ihb.ac.cn/visomics. Source code can be downloaded at http://bioinfo.ihb.ac.cn/software/visomics/visomics-1.1.tar.gz. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Mijuan Shi, Keyi Ren, Weidong Ye, Wanting Zhang, Yingyin Cheng, Xiaoqin Xia
Bioinform.3