2002/07/01 by Amos Tanay, Roded Sharan, Ron Shamir · 7 citations
Biochemistry, Genetics and Molecular Biology · #Gene expression and cancer classification #Bioinformatics and Genomic Networks #Gene Regulatory Network Analysis #Expression (computer science) #Gene expression #Computational biology #Gene #Biology #Computer science #Genetics #Data mining
paper · doi:10.1093/bioinformatics/18.suppl_1.s136
openalex publication_date 2002/07/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/26
In gene expression data, a bicluster is a subset of the genes exhibiting consistent patterns over a subset of the conditions. We propose a new method to detect significant biclusters in large expression datasets. Our approach is graph theoretic coupled with statistical modelling of the data. Under plausible assumptions, our algorithm is polynomial and is guaranteed to find the most significant biclusters. We tested our method on a collection of yeast expression profiles and on a human cancer dataset. Cross validation results show high specificity in assigning function to genes based on their biclusters, and we are able to annotate in this way 196 uncharacterized yeast genes. We also demonstrate how the biclusters lead to detecting new concrete biological associations. In cancer data we are able to detect and relate finer tissue types than was previously possible. We also show that the method outperforms the biclustering algorithm of Cheng and Church (2000).