Evidence map›Paper›PMID 33219674›Full record

ArticleNucleic acids research2021

canSAR: update to the cancer translational research and drug discovery knowledgebase.

Costas Mitsopoulos, Patrizio Di Micco, Eloy Villasclaras Fernandez, Daniela Dolciami, Esty Holt, Ioan L Mica, Elizabeth A Coker, Joseph E Tym, James Campbell, Ka Hing Che and 5 more

Abstract read
In one paragraph

Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.

0numbers the graph read from it
0cells of the map it votes in
58citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

58 citing papers in PubMed.

  1. Article
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  6. Review
  7. Cancer pharmacoinformatics: Databases and analytical tools.Functional & integrative genomics · 2024
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Costas MitsopoulosDepartment of Data Science, The Institute of Cancer Research, London SM2 5NG, UK.
Patrizio Di MiccoDepartment of Data Science, The Institute of Cancer Research, London SM2 5NG, UK.
Eloy Villasclaras FernandezDepartment of Data Science, The Institute of Cancer Research, London SM2 5NG, UK.
Daniela DolciamiDepartment of Data Science, The Institute of Cancer Research, London SM2 5NG, UK.
Esty HoltDepartment of Data Science, The Institute of Cancer Research, London SM2 5NG, UK.
Ioan L MicaDepartment of Data Science, The Institute of Cancer Research, London SM2 5NG, UK.
Elizabeth A CokerDepartment of Data Science, The Institute of Cancer Research, London SM2 5NG, UK.
Joseph E TymDepartment of Data Science, The Institute of Cancer Research, London SM2 5NG, UK.
James CampbellDepartment of Data Science, The Institute of Cancer Research, London SM2 5NG, UK.
Ka Hing CheCancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research, London SM2 5NG, UK.
Bugra OzerDepartment of Data Science, The Institute of Cancer Research, London SM2 5NG, UK.
Christos KannasDepartment of Data Science, The Institute of Cancer Research, London SM2 5NG, UK.
Albert A AntolinDepartment of Data Science, The Institute of Cancer Research, London SM2 5NG, UK.
Paul WorkmanCancer Research UK Cancer Therapeutics Unit, The Institute of Cancer Research, London SM2 5NG, UK.
Bissan Al-LazikaniDepartment of Data Science, The Institute of Cancer Research, London SM2 5NG, UK.

Funding

Cancer Research UK 23187Cancer Research UK C309/A25144Cancer Research UK C35696/A23187Wellcome Trust 204735/Z/16/ZWellcome Trust WT204735/Z/16/ZWellcome Trust WT212969/Z/18/Z
6 · The paper itself

Abstract

canSAR (http://cansar.icr.ac.uk) is the largest, public, freely available, integrative translational research and drug discovery knowledgebase for oncology. canSAR integrates vast multidisciplinary data from across genomic, protein, pharmacological, drug and chemical data with structural biology, protein networks and more. It also provides unique data, curation and annotation and crucially, AI-informed target assessment for drug discovery. canSAR is widely used internationally by academia and industry. Here we describe significant developments and enhancements to the data, web interface and infrastructure of canSAR in the form of the new implementation of the system: canSARblack. We demonstrate new functionality in aiding translation hypothesis generation and experimental design, and show how canSAR can be adapted and utilised outside oncology.

Indexed as

Databases, GeneticKnowledge BasesAntineoplastic AgentsComputational BiologyData MiningDrug DiscoveryGenomicsHumansInternetMedical OncologyMolecular StructureNeoplasmsProteomicsTranslational Research, BiomedicalUser-Computer InterfaceAntineoplastic Agents

Identifiers

PMID33219674
PMCPMC7778970

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.