ArticleJournal of proteome research2021
Simplified and Unified Access to Cancer Proteogenomic Data.
Article in Journal of proteome research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed, 37 citations in OpenAlex.
- A plasma derived hypoxia signature predicts benefit from hypoxia-modifying radiotherapy and reveals dynamic humoral immune modulation in bladder cancer.British journal of cancer · 2026Article
- Article
- Domain generalisation challenges in breast cancer molecular classification using foundation models: a cross-cohort exploratory study.Medical & biological engineering & computing · 2026Article
- Enabling cross-indication protein expression analysis using a curated pan-cancer dataset and a tailored workflow.Scientific reports · 2026Article
- Single-cell and spatial transcriptomic characterization of pulmonary pleomorphic carcinoma.Communications biology · 2025Article
- Glycoprotein-Notebook: A Pan-Cancer Glycoproteomic Database and Toolkit for Analysis of Protein Glycosylation Changes Associated With Cancer Phenotypes.Molecular & cellular proteomics : MCP · 2025Article
- Multi-omics protein signaling networks identify sex-specific therapeutic candidates in lung adenocarcinoma.Biology of sex differences · 2025Article
- Informatics at the Frontier of Cancer Research.Cancer research · 2025Review
- Activating Cancer Hallmarks through Changes in mRNA/Protein Regulation.Journal of proteome research · 2025Article
- Identifying Sex Differences in Lung Adenocarcinoma Using Multi-Omics Integrative Protein Signaling Networks.bioRxiv : the preprint server for biology · 2025Article
- Multi-dataset Integration and Residual Connections Improve Proteome Prediction from Transcriptomes using Deep Learning.bioRxiv : the preprint server for biology · 2024Article
- Decomprolute is a benchmarking platform designed for multiomics-based tumor deconvolution.Cell reports methods · 2024Article
- Article
- Evaluating a large language model's ability to solve programming exercises from an introductory bioinformatics course.PLoS computational biology · 2023Article
- Review
- Proteogenomic data and resources for pan-cancer analysis.Cancer cell · 2023Review
- Epigenetic Silencing ofCancers · 2023Article
- Identification of BGN and THBS2 as metastasis-specific biomarkers and poor survival key regulators in human colon cancer by integrated analysis.Clinical and translational medicine · 2022Article
- Protein prediction models support widespread post-transcriptional regulation of protein abundance by interacting partners.PLoS computational biology · 2022Article
- Experimental reproducibility limits the correlation between mRNA and protein abundances in tumor proteomic profiles.Cell reports methods · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Comprehensive cancer data sets recently generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) offer great potential for advancing our understanding of how to combat cancer. These data sets include DNA, RNA, protein, and clinical characterization for tumor and normal samples from large cohorts of many different cancer types. The raw data are publicly available at various Cancer Research Data Commons. However, widespread reuse of these data sets is also facilitated by easy access to the processed quantitative data tables. We have created a data application programming interface (API) to distribute these processed tables, implemented as a Python package called cptac. We implement it such that users who prefer to work in R can easily use our package for data access and then transfer the data into R for analysis. Our package distributes the finalized processed CPTAC data sets in a consistent, up-to-date format. This consistency makes it easy to integrate the data with common graphing, statistical, and machine-learning packages for advanced analysis. Additionally, consistent formatting across all cancer types promotes the investigation of pan-cancer trends. The data API structure of directly streaming data within a programming environment enhances the reproducibility. Finally, with the accompanying tutorials, this package provides a novel resource for cancer research education. View the software documentation at https://paynelab.github.io/cptac/. View the GitHub repository at https://github.com/PayneLab/cptac.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.