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Proc AMIA Symp. 2002 : 682–686.
PMCID: PMC1357234
NIHMSID: NIHMS6744

An unsupervised self-optimizing gene clustering algorithm.

Abstract

We have devised a gene-clustering algorithm that is completely unsupervised in that no parameters need be set by the user, and the clustering of genes is self-optimizing to yield the set of clusters that minimizes within-cluster distance and maximizes between-cluster distance. This algorithm was implemented in Java, and tested on a randomly selected 200-gene subset of 3000 genes from cell-cycle data in S. cerevisiae. AlignACE was used to evaluate the resulting optimized cluster set for upstream cis-regulons. The optimized cluster set was found to be of comparable quality to cluster sets obtained by two established methods (complete linkage and k-means), even when provided with only a small, randomly selected subset of the data (200 vs 3000 genes), and with absolutely no supervision. MAP and specificity scores of the highest ranking motifs identified in the largest clusters were comparable.

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Selected References

These references are in PubMed. This may not be the complete list of references from this article.
  • Tavazoie S, Hughes JD, Campbell MJ, Cho RJ, Church GM. Systematic determination of genetic network architecture. Nat Genet. 1999 Jul;22(3):281–285. [PubMed]
  • Hughes JD, Estep PW, Tavazoie S, Church GM. Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae. J Mol Biol. 2000 Mar 10;296(5):1205–1214. [PubMed]
  • Manson McGuire A, Church GM. Predicting regulons and their cis-regulatory motifs by comparative genomics. Nucleic Acids Res. 2000 Nov 15;28(22):4523–4530. [PMC free article] [PubMed]

Articles from Proceedings of the AMIA Symposium are provided here courtesy of American Medical Informatics Association