ArticlebioRxiv : the preprint server for biology2024
Integrative Computational Framework,
Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Though somatic mutations play a critical role in driving cancer initiation and progression, the systems-level functional impacts of these mutations-particularly, how they alter expression across the genome and give rise to cancer hallmarks-are not yet well-understood, even for well-studied cancer driver genes. To address this, we designed an integrative machine learning model, Dyscovr, that leverages mutation, gene expression, copy number alteration (CNA), methylation, and clinical data to uncover putative relationships between nonsynonymous mutations in key cancer driver genes and transcriptional changes across the genome. We applied Dyscovr pan-cancer and within 19 individual cancer types, finding both broadly relevant and cancer type-specific links between driver genes and putative targets, including a subset we further identify as exhibiting negative genetic relationships. Our work newly implicates-and validates in cell lines-
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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.