ArticleCancer discovery2022
Developmental Deconvolution Suggests New Tumor Biology and a Tool for Predicting Cancer Origin.
Article in Cancer discovery, 2022. 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
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Who cites it
1 citing paper in PubMed, 1 citations in OpenAlex.
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1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
summaryDefining the developmental origins of cancer can help uncover cellular mechanisms of cancer development and progression and identify effective treatments, but it has been challenging. In this issue of Cancer Discovery, Moiso and colleagues constructed a developmental map of 33 cancer types, based on which they deconvoluted tumors into developmental components and constructed a deep learning classifier capable of high- accuracy tumor type prediction. See related article by Moiso et al., p. 2566 (4) .
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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.