ReviewCancer informatics2015
Cancer Bioinformatic Methods to Infer Meaningful Data From Small-Size Cohorts.
Review in Cancer informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Accuracy of remote diagnosis of oral mucositis in children and adolescents with cancer using a mobile App: a pilot study.Brazilian oral research · 2026Article
- Divergent roles of PKM2 in regulating PD-L1 and PD-L2 expression and their implications in human and mouse cancer models.Acta biochimica et biophysica Sinica · 2025Article
- Prognostic Value of BIRC5 in Lung Adenocarcinoma Lacking EGFR, KRAS, and ALK Mutations by Integrated Bioinformatics Analysis.Disease markers · 2019Article
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
Whole-genome analyses have uncovered that most cancer-relevant genes cluster into 12 signaling pathways. Knowledge of the signaling pathways and associated gene signatures not only allows us to understand the mechanisms of oncogenesis inherent to specific cancers but also provides us with drug targets, molecular diagnostic and prognosis factors, as well as biomarkers for patient risk stratification and treatment. Publicly available genomic data sets constitute a wealth of gene mining opportunities for hypothesis generation and testing. However, the increasingly recognized genetic and epigenetic inter- and intratumor heterogeneity, combined with the preponderance of small-size cohorts, hamper reliable analysis and discovery. Here, we review two methods that are used to infer meaningful biological events from small-size data sets and discuss some of their applications and limitations.
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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.