ArticleAlgorithms for molecular biology : AMB2010
Differential co-expression framework to quantify goodness of biclusters and compare biclustering algorithms.
Article in Algorithms for molecular biology : AMB, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Rank-preserving biclustering algorithm: a case study on miRNA breast cancer.Medical & biological engineering & computing · 2021Article
- It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data.Briefings in bioinformatics · 2019Review
- MultiDCoX: Multi-factor analysis of differential co-expression.BMC bioinformatics · 2017Article
- Constructing module maps for integrated analysis of heterogeneous biological networks.Nucleic acids research · 2014Article
- A comparative analysis of biclustering algorithms for gene expression data.Briefings in bioinformatics · 2013Article
- Scalable high-throughput identification of genetic targets by network filtering.BMC bioinformatics · 2013Article
- Whole miRNome-wide differential co-expression of microRNAs.Genomics, proteomics & bioinformatics · 2012Article
- A comparison and evaluation of five biclustering algorithms by quantifying goodness of biclusters for gene expression data.BioData mining · 2012Article
- Exploring tomato gene functions based on coexpression modules using graph clustering and differential coexpression approaches.Plant physiology · 2012Article
- DeBi: Discovering Differentially Expressed Biclusters using a Frequent Itemset Approach.Algorithms for molecular biology : AMB · 2011Article
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
backgroundBiclustering is an important analysis procedure to understand the biological mechanisms from microarray gene expression data. Several algorithms have been proposed to identify biclusters, but very little effort was made to compare the performance of different algorithms on real datasets and combine the resultant biclusters into one unified ranking.
resultsIn this paper we propose differential co-expression framework and a differential co-expression scoring function to objectively quantify quality or goodness of a bicluster of genes based on the observation that genes in a bicluster are co-expressed in the conditions belonged to the bicluster and not co-expressed in the other conditions. Furthermore, we propose a scoring function to stratify biclusters into three types of co-expression. We used the proposed scoring functions to understand the performance and behavior of the four well established biclustering algorithms on six real datasets from different domains by combining their output into one unified ranking.
conclusionsDifferential co-expression framework is useful to provide quantitative and objective assessment of the goodness of biclusters of co-expressed genes and performance of biclustering algorithms in identifying co-expression biclusters. It also helps to combine the biclusters output by different algorithms into one unified ranking i.e. meta-biclustering.
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.