SynthesisCancer medicine2015
Multiple criteria optimization joint analyses of microarray experiments in lung cancer: from existing microarray data to new knowledge.
Synthesis in Cancer medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
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, 2 syntheses or guidelines pooled it.
- Hepatitis C virus infection and Parkinson's disease: insights from a joint sex-stratified BioOptimatics meta-analysis.Scientific reports · 2024Pooled it
- Multiple criteria optimization joint analyses of microarray experiments in lung cancer: from existing microarray data to new knowledge.Cancer medicine · 2015Pooled it
- Multiple Criteria Optimization (MCO): A gene selection deterministic tool in RStudio.PloS one · 2022Article
- A Selection of Important Genes and Their Correlated Behavior in Alzheimer's Disease.Journal of Alzheimer's disease : JAD · 2018Article
- Systems Biomedicine of Rabies Delineates the Affected Signaling Pathways.Frontiers in microbiology · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Microarrays can provide large amounts of data for genetic relative expression in illnesses of interest such as cancer in short time. These data, however, are stored and often times abandoned when new experimental technologies arrive. This work reexamines lung cancer microarray data with a novel multiple criteria optimization-based strategy aiming to detect highly differentially expressed genes. This strategy does not require any adjustment of parameters by the user and is capable to handle multiple and incommensurate units across microarrays. In the analysis, groups of samples from patients with distinct smoking habits (never smoker, current smoker) and different gender are contrasted to elicit sets of highly differentially expressed genes, several of which are already associated to lung cancer and other types of cancer. The list of genes is provided with a discussion of their role in cancer, as well as the possible research directions for each of them.
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.