ArticleNucleic acids research2015
Cancer3D: understanding cancer mutations through protein structures.
Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
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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.
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Who cites it
38 citing papers in PubMed, 52 citations in OpenAlex.
- Interpreting human genetic variation at atomic resolution.Nature genetics · 2026Review
- Cat-E: A comprehensive web tool for exploring cancer targeting strategies.Computational and structural biotechnology journal · 2024Article
- Singleton mutations in large-scale cancer genome studies: uncovering the tail of cancer genome.NAR cancer · 2024Article
- Recent Progress of Protein Tertiary Structure Prediction.Molecules (Basel, Switzerland) · 2024Review
- Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies.Current atherosclerosis reports · 2023Review
- Pan-cancer clinical impact of latent drivers from double mutations.Communications biology · 2023Article
- VIVID: A Web Application for Variant Interpretation and Visualization in Multi-dimensional Analyses.Molecular biology and evolution · 2022Article
- Tailoring Multi-omics to Inflammatory Bowel Diseases: All for One and One for All.Journal of Crohn's & colitis · 2022Article
- Toward the solution of the protein structure prediction problem.The Journal of biological chemistry · 2021Review
- PJAMIA open · 2021Article
- Pathogenic missense protein variants affect different functional pathways and proteomic features than healthy population variants.PLoS biology · 2021Article
- Swiss-PO: a new tool to analyze the impact of mutations on protein three-dimensional structures for precision oncology.NPJ precision oncology · 2021Article
- Leveraging biochemical reactions to unravel functional impacts of cancer somatic variants affecting protein interaction interfaces.F1000Research · 2021Article
- TGPred: a tumor gene prediction webserver for analyzing structural and functional impacts of variants.Journal of molecular cell biology · 2020Article
- An Effective Graph Clustering Method to Identify Cancer Driver Modules.Frontiers in bioengineering and biotechnology · 2020Article
- Classification of VUS and unclassified variants inComputational and structural biotechnology journal · 2020Article
- A survey and evaluation of Web-based tools/databases for variant analysis of TCGA data.Briefings in bioinformatics · 2019Review
- Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers.PLoS computational biology · 2019Review
- The landscape of selection in 551 esophageal adenocarcinomas defines genomic biomarkers for the clinic.Nature genetics · 2019Article
- Personal Mutanomes Meet Modern Oncology Drug Discovery and Precision Health.Pharmacological reviews · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
The new era of cancer genomics is providing us with extensive knowledge of mutations and other alterations in cancer. The Cancer3D database at http://www.cancer3d.org gives an open and user-friendly way to analyze cancer missense mutations in the context of structures of proteins in which they are found. The database also helps users analyze the distribution patterns of the mutations as well as their relationship to changes in drug activity through two algorithms: e-Driver and e-Drug. These algorithms use knowledge of modular structure of genes and proteins to separately study each region. This approach allows users to find novel candidate driver regions or drug biomarkers that cannot be found when similar analyses are done on the whole-gene level. The Cancer3D database provides access to the results of such analyses based on data from The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE). In addition, it displays mutations from over 14,700 proteins mapped to more than 24,300 structures from PDB. This helps users visualize the distribution of mutations and identify novel three-dimensional patterns in their distribution.
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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.