ArticleCell2018
Chemistry Takes Center Stage for Identifying Cancer Targetability.
Article in Cell, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
- Comment on
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Matching genetically defined cancer states to drugs that specifically target these states is a principal goal of personalized oncology medicine. In this issue, McMillan et al. show how large-scale chemical screening coupled to deep molecular profiling can identify mechanistically diverse druggable vulnerabilities for genetic subtypes of lung cancers.
Indexed as
Identifiers
29727668What 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.