ReviewThe Yale journal of biology and medicine2012
Systematic deciphering of cancer genome networks.
Review in The Yale journal of biology and medicine, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed.
- Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers.PLoS computational biology · 2019Review
- The structural basis for cancer treatment decisions.Oncotarget · 2014Review
- Systems genetics approaches to understand complex traits.Nature reviews. Genetics · 2014Review
- The role of peroxiredoxin II in chemoresistance of breast cancer cells.Breast cancer (Dove Medical Press) · 2014Review
- Ovarian cancer stem cells: a new target for cancer therapy.BioMed research international · 2013Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
When growth regulatory genes are damaged in a cell, it may become cancerous. Current technological advances in the last decade have allowed the characterization of the whole genome of these cells by directly or indirectly measuring DNA changes. Complementary analyses were developed to make sense of the massive amounts of data generated. A large majority of these analyses were developed to construct interaction networks between genes from, primarily, expression array data. We review the current technologies and analyses that have developed in the last decade. We further argue that as cancer genomics evolves from single gene validations to gene network inferences, new analyses must be developed for the different technological platforms.
Indexed as
Identifiers
23012582PMC3447198What 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.