ReviewBiology direct2012
Harnessing the complexity of gene expression data from cancer: from single gene to structural pathway methods.
Review in Biology direct, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Review
- Precision Prediction of Alzheimer's Disease: Integrating Mitochondrial Energy Metabolism and Immunological Insights.Journal of molecular neuroscience : MN · 2025Article
- Detection of genes with differential expression dispersion unravels the role of autophagy in cancer progression.PLoS computational biology · 2023Article
- Predicting and affecting response to cancer therapy based on pathway-level biomarkers.Nature communications · 2020Article
- Profiling of differentially expressed genes in cadmium-induced prostate carcinogenesis.Toxicology and applied pharmacology · 2019Article
- Article
- A knowledge-based T2-statistic to perform pathway analysis for quantitative proteomic data.PLoS computational biology · 2017Article
- Review
- Effects of subsampling on characteristics of RNA-seq data from triple-negative breast cancer patients.Chinese journal of cancer · 2015Article
- Report from the 2nd Summer School in Computational Biology organized by the Queen's University of Belfast.Genomics data · 2014Article
- Network-based biomarkers enhance classical approaches to prognostic gene expression signatures.BMC systems biology · 2014Article
- The gene regulatory network for breast cancer: integrated regulatory landscape of cancer hallmarks.Frontiers in genetics · 2014Article
- Enhancing systems medicine beyond genotype data by dynamic patient signatures: having information and using it too.Frontiers in genetics · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-dimensional gene expression data provide a rich source of information because they capture the expression level of genes in dynamic states that reflect the biological functioning of a cell. For this reason, such data are suitable to reveal systems related properties inside a cell, e.g., in order to elucidate molecular mechanisms of complex diseases like breast or prostate cancer. However, this is not only strongly dependent on the sample size and the correlation structure of a data set, but also on the statistical hypotheses tested. Many different approaches have been developed over the years to analyze gene expression data to (I) identify changes in single genes, (II) identify changes in gene sets or pathways, and (III) identify changes in the correlation structure in pathways. In this paper, we review statistical methods for all three types of approaches, including subtypes, in the context of cancer data and provide links to software implementations and tools and address also the general problem of multiple hypotheses testing. Further, we provide recommendations for the selection of such analysis methods.
Indexed as
Identifiers
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.