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Zefang Tang

dblp:197/8187 · DBLP profile ↗
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4ranked-venue papers
3as first author
2since 2021 · last 2024
0000-0002-5264-9560ORCID · corroborated

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

Applied, interdisciplinary, general and emerging computing · 3 · 3 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 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%

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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
gene expression analysis
0.312017
GE-mini: a mobile APP for large-scale gene expression visualization · Bioinform. 2017
Bioinformatics and computational biology › gene expression analysis
gene expression visualization
0.312017
GE-mini: a mobile APP for large-scale gene expression visualization · Bioinform. 2017
Bioinformatics and computational biology
cancer genomics
0.112017
GE-mini: a mobile APP for large-scale gene expression visualization · Bioinform. 2017

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

mobile application · 0.3
YearPublicationVenuePosition
2024 High-density foreground object detection in optical remote sensing images via semantic fusion and box alignment
Shuzhi Su, Zefang Tang
Vis. Comput.2
2021 Disease network delineates the disease progression profile of cardiovascular diseases
Zefang Tang, Yiqin Yu, Kenney Ng, Daby M. Sow, Jianying Hu, Jing Mei
J. Biomed. Informatics1
2020 Embracing Disease Progression with a Learning System for Real World Evidence Discovery
Zefang Tang, Lun Hu, Xu Min, Jing Mei, Kenney Ng, Shaochun Li, Pengwei Hu 0001, Zhu-Hong You
ICIC (2)1
2017 GE-mini: a mobile APP for large-scale gene expression visualization
abstract
Summary: The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) projects produced large-scale RNA sequencing data, which provides an opportunity for performing integrated expression analysis for all genes across tens of thousands of tumor and normal tissue specimens. Rapid access to and easy visualization of such valuable data could facilitate research in a wide biological area. Here, we present the GE-mini APP for smart phones, a mobile visualization tool for integrated gene expression data based on both TCGA and GTEx. This gene-centric expression viewer provides a convenient method for displaying expression profiles of all available tumor and tissue types, while allowing drilling down to detailed views for specific tissue types. Availability and Implementation: Both the iOS and Android APPs are freely available to all non-commercial users in App Store and Google Play. The QR codes of App store and Google play are also provided for scanning and download. The GE-mini web server is also available at http://gemini.cancer-pku.cn/ . Contacts: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.
Zefang Tang, Chenwei Li, Karena Zhang, Xueda Hu
Bioinform.1