Literature DB >> 25635265

Mining Association Rules among Gene Functions in Clusters of Similar Gene Expression Maps.

Li An1, Zoran Obradovic2, Desmond Smith3, Olivier Bodenreider4, Vasileios Megalooikonomou.   

Abstract

Association rules mining methods have been recently applied to gene expression data analysis to reveal relationships between genes and different conditions and features. However, not much effort has focused on detecting the relation between gene expression maps and related gene functions. Here we describe such an approach to mine association rules among gene functions in clusters of similar gene expression maps on mouse brain. The experimental results show that the detected association rules make sense biologically. By inspecting the obtained clusters and the genes having the gene functions of frequent itemsets, interesting clues were discovered that provide valuable insight to biological scientists. Moreover, discovered association rules can be potentially used to predict gene functions based on similarity of gene expression maps.

Entities:  

Keywords:  association rules mining; clustering; gene expression maps; gene functions; voxelation

Year:  2009        PMID: 25635265      PMCID: PMC4307020          DOI: 10.1109/BIBMW.2009.5332104

Source DB:  PubMed          Journal:  IEEE Int Conf Bioinform Biomed Workshops


  4 in total

1.  A genome-scale map of expression for a mouse brain section obtained using voxelation.

Authors:  Mark H Chin; Alex B Geng; Arshad H Khan; Wei-Jun Qian; Vladislav A Petyuk; Jyl Boline; Shawn Levy; Arthur W Toga; Richard D Smith; Richard M Leahy; Desmond J Smith
Journal:  Physiol Genomics       Date:  2007-05-15       Impact factor: 3.107

Review 2.  Exploring the new world of the genome with DNA microarrays.

Authors:  P O Brown; D Botstein
Journal:  Nat Genet       Date:  1999-01       Impact factor: 38.330

3.  Analysis of multiplex gene expression maps obtained by voxelation.

Authors:  Li An; Hongbo Xie; Mark H Chin; Zoran Obradovic; Desmond J Smith; Vasileios Megalooikonomou
Journal:  BMC Bioinformatics       Date:  2009-04-29       Impact factor: 3.169

4.  Fuzzy association rules for biological data analysis: a case study on yeast.

Authors:  Francisco J Lopez; Armando Blanco; Fernando Garcia; Carlos Cano; Antonio Marin
Journal:  BMC Bioinformatics       Date:  2008-02-19       Impact factor: 3.169

  4 in total
  1 in total

1.  Mining rare associations between biological ontologies.

Authors:  Fernando Benites; Svenja Simon; Elena Sapozhnikova
Journal:  PLoS One       Date:  2014-01-03       Impact factor: 3.240

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.