Evidence map›Paper›PMID 34902126›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2022

Unsupervised Algorithms for Microarray Sample Stratification.

Michele Fratello, Luca Cattelani, Antonio Federico, Alisa Pavel, Giovanni Scala, Angela Serra, Dario Greco

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Michele FratelloFaculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
Luca CattelaniFaculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
Antonio FedericoFaculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
Alisa PavelFaculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
Giovanni ScalaDepartment of Biology, University of Naples Federico II, Naples, Italy.
Angela SerraFaculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
Dario GrecoFaculty of Medicine and Health Technology, Tampere University, Tampere, Finland. dario.greco@tuni.fi.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The amount of data made available by microarrays gives researchers the opportunity to delve into the complexity of biological systems. However, the noisy and extremely high-dimensional nature of this kind of data poses significant challenges. Microarrays allow for the parallel measurement of thousands of molecular objects spanning different layers of interactions. In order to be able to discover hidden patterns, the most disparate analytical techniques have been proposed. Here, we describe the basic methodologies to approach the analysis of microarray datasets that focus on the task of (sub)group discovery.

Indexed as

AlgorithmsCluster AnalysisGene Expression ProfilingOligonucleotide Array Sequence AnalysisClusteringDimensionality reductionGroup discoveryMicroarrayUnsupervised learning

Identifiers

PMID34902126

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.