Avery Wang

1.5k citations
8 papers · 896 · 2 hit papers · h-index 6

Impact in

    • Cancer Genomics and Diagnostics
    • Breast Cancer Treatment Studies
    • Artificial Intelligence in Healthcare and Education

Papers in

Avery Wang

7 papers receiving 872 citations

Avery Wang's Hit Papers

Automated real-world data integration improves cancer outcome prediction 2024 · 94 citations
940+1+2Years since publication100200300400500

Peers

Avery Wang
Comparison fields: 5 of 73
  • Cancer Research 180
  • Health Informatics 30
  • Signal Processing 92
  • Oncology 190
  • Pulmonary and Respiratory Medicine 118
Replace Tai-Hsien Ou Yang with:
Tai-Hsien Ou Yang United States
Miika J. Ahdesmaki United Kingdom
Zhenxiang Li China
Kristy Choi United States
Wei Song China
Matthias Hüser Switzerland
Stephanie A. Nevins United States
Robert Krueger United States
Patrik Edén Sweden
Xiaobo Zhou United States
Avery Wang relative to Tai-Hsien Ou Yang United States Tai-Hsien Ou Yang's profile →
Citations per field
00.5×2×4×6×8.4×
Tai-Hsien Ou Yang · 1×
Citations per year

Countries citing papers authored by Avery Wang

Since Specialization
Citations

This map shows the geographic impact of Avery Wang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Avery Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Avery Wang more than expected).

Fields of papers citing papers by Avery Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Avery Wang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Avery Wang. The network helps show where Avery Wang may publish in the future.

Co-authors

The 3 scholars most cited alongside Avery Wang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Avery Wang Line = papers co-authored together Avery Wang links everyone, so they are left out of the graph.

All Works

8 of 8 papers shown
#Work
1
Analysis and Visualization of Longitudinal Genomic and Clinical Data from the AACR Project GENIE Biopharma Collaborative in cBioPortal
Hit paper breakdown →
2023524
2 2006155
3 202199
4
Automated real-world data integration improves cancer outcome prediction
Hit paper breakdown →
202494
5 202212
6 20228
7 20244
8 20250

About Avery Wang

Avery Wang is a scholar working on Cancer Research, Signal Processing, Genetics, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging, having authored 8 papers that have together received 896 indexed citations. Recurring topics across this work include Cancer Genomics and Diagnostics (5 papers), Lung Cancer Treatments and Mutations (1 paper), AI in cancer detection (1 paper), Biomedical Text Mining and Ontologies (1 paper), Music and Audio Processing (1 paper), Glioma Diagnosis and Treatment (1 paper), Lung Cancer Research Studies (1 paper) and Radiomics and Machine Learning in Medical Imaging (1 paper). The work is most often cited by research in Cancer Research (180 citations), Health Informatics (30 citations), Signal Processing (92 citations), Oncology (190 citations) and Pulmonary and Respiratory Medicine (118 citations). Avery Wang has collaborated with scholars based in United States, Netherlands and Canada. Frequent co-authors include David H. Gutmann, Jit Chatterjee and Yangming Ou. Their work appears in journals such as JCO Clinical Cancer Informatics, Communications of the ACM, Nature, Cancer Research and Neuro-Oncology.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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