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Cited by 512

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

TCPA: a resource for cancer functional proteomics data

Open access
Li, Jun, Lu, Yiling, Akbani, Rehan, Ju, Zhenlin, Roebuck, Paul L, Liu, Wenbin, Yang, Ji-Yeon, Broom, Bradley M, Verhaak, Roeland G W, Kane, David W, Wakefield, Chris, Weinstein, John N, Mills, Gordon B, Liang, Han·2013·Nature Methods

Abstract

Functional proteomics represents a powerful approach to understand the pathophysiology and therapy of cancer. However, comprehensive cancer proteomic data have been relatively limited. As a part of The Cancer Genome Atlas (TCGA) Project and other efforts, we have generated protein expression data over a large number of tumor and cell line samples using reverse-phase protein arrays (RPPAs). RPPA is a quantitative, antibody-based technology that can assess multiple protein markers in many samples in a cost-effective, sensitive and high-throughput manner 1 , 2 . This technology has been extensively validated for both cell line and patient samples 3 , 4 , 5 , and its applications range from building reproducible prognostic models 6 to generating experimentally verified mechanistic insights 7 .

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Metadata

Authors
Li, J., Lu, Y., Akbani, R., Ju, Z., Roebuck, P. L., Liu, W., Yang, J.-Y., Broom, B. M., Verhaak, R. G. W., Kane, D. W., Wakefield, C., Weinstein, J. N., Mills, G. B., & Liang, H.
Venue
Nature Methods
Published
2013-09-15
Volume
10
Issue
11
Pages
1046-1047
ISSN
  • 1548-7091 (ISSN-L)
  • 1548-7105
OpenAlex
W1979340580
Cited by
512
References
10
FWCI
9.01
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: TCPA: a resource for cancer functional proteomics data. https://app.cochat.ai/papers/10.1038/nmeth.2650
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Li, J., Lu, Y., Akbani, R., Ju, Z., Roebuck, P. L., Liu, W., Yang, J.-Y., Broom, B. M., Verhaak, R. G. W., Kane, D. W., Wakefield, C., Weinstein, J. N., Mills, G. B., & Liang, H. (2013). TCPA: a resource for cancer functional proteomics data. Nature Methods. https://doi.org/10.1038/nmeth.2650

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.