ArticleOncogenesis2015
Atlas of Cancer Signalling Network: a systems biology resource for integrative analysis of cancer data with Google Maps.
Article in Oncogenesis, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
71 citing papers in PubMed, 156 citations in OpenAlex.
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- A guide for developing comprehensive systems biology maps of disease mechanisms: planning, construction and maintenance.Frontiers in bioinformatics · 2023Article
- Hybrid computational modeling highlights reverse warburg effect in breast cancer-associated fibroblasts.Computational and structural biotechnology journal · 2023Article
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- Innovations in Genomics and Big Data Analytics for Personalized Medicine and Health Care: A Review.International journal of molecular sciences · 2022Review
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- Modeling signaling pathways in biology with MaBoSS: From one single cell to a dynamic population of heterogeneous interacting cells.Computational and structural biotechnology journal · 2022Review
- Humans and machines in biomedical knowledge curation: hypertrophic cardiomyopathy molecular mechanisms' representation.BioData mining · 2021Article
- SBGN Bricks Ontology as a tool to describe recurring concepts in molecular networks.Briefings in bioinformatics · 2021Article
- Reusability and composability in process description maps: RAS-RAF-MEK-ERK signalling.Briefings in bioinformatics · 2021Article
- The status of causality in biological databases: data resources and data retrieval possibilities to support logical modeling.Briefings in bioinformatics · 2021Article
11 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancerogenesis is driven by mutations leading to aberrant functioning of a complex network of molecular interactions and simultaneously affecting multiple cellular functions. Therefore, the successful application of bioinformatics and systems biology methods for analysis of high-throughput data in cancer research heavily depends on availability of global and detailed reconstructions of signalling networks amenable for computational analysis. We present here the Atlas of Cancer Signalling Network (ACSN), an interactive and comprehensive map of molecular mechanisms implicated in cancer. The resource includes tools for map navigation, visualization and analysis of molecular data in the context of signalling network maps. Constructing and updating ACSN involves careful manual curation of molecular biology literature and participation of experts in the corresponding fields. The cancer-oriented content of ACSN is completely original and covers major mechanisms involved in cancer progression, including DNA repair, cell survival, apoptosis, cell cycle, EMT and cell motility. Cell signalling mechanisms are depicted in detail, together creating a seamless 'geographic-like' map of molecular interactions frequently deregulated in cancer. The map is browsable using NaviCell web interface using the Google Maps engine and semantic zooming principle. The associated web-blog provides a forum for commenting and curating the ACSN content. ACSN allows uploading heterogeneous omics data from users on top of the maps for visualization and performing functional analyses. We suggest several scenarios for ACSN application in cancer research, particularly for visualizing high-throughput data, starting from small interfering RNA-based screening results or mutation frequencies to innovative ways of exploring transcriptomes and phosphoproteomes. Integration and analysis of these data in the context of ACSN may help interpret their biological significance and formulate mechanistic hypotheses. ACSN may also support patient stratification, prediction of treatment response and resistance to cancer drugs, as well as design of novel treatment strategies.
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.