arXiv subjects

G. Getz

Publications and source records attributed to G. Getz.

3 recordsLinked to original sources

arXiv2005

Expression profiles of acute lymphoblastic and myeloblastic leukemias with ALL-1 rearrangements

The ALL-1 gene is directly involved in 5-10% of ALLs and AMLs by fusion to other genes or through internal rearrangements. DNA microarrays were utilized to determine expression profiles of ALLs and AMLs with ALL-1 rearrangements. These profiles distinguish those tumors from other ALLs and AMLs. The expression patterns of ALL-1-associated tumors, in particular ALLs, involve oncogenes, tumor suppressors, anti apoptotic genes, drug resistance genes etc., and correlate with the aggressive nature of the tumors. The genes whose expression differentiates between ALLs with and without ALL-1 rearrangement were further divided into several groups enabling separation of ALL-1- associated ALLs into two subclasses. Further, AMLs with partial duplication of ALL-1 vary in their expression pattern from AMLs in which ALL-1 had undergone fusion to other genes. The extensive analysis described here draws attention to genes which might have a direct role in pathogenesis.

q-bio.QM↗

arXiv2000

Coupled Two-Way Clustering Analysis of Gene Microarray Data

We present a novel coupled two-way clustering approach to gene microarray data analysis. The main idea is to identify subsets of the genes and samples, such that when one of these is used to cluster the other, stable and significant partitions emerge. The search for such subsets is a computationally complex task: we present an algorithm, based on iterative clustering, which performs such a search. This analysis is especially suitable for gene microarray data, where the contributions of a variety of biological mechanisms to the gene expression levels are entangled in a large body of experimental data. The method was applied to two gene microarray data sets, on colon cancer and leukemia. By identifying relevant subsets of the data and focusing on them we were able to discover partitions and correlations that were masked and hidden when the full dataset was used in the analysis. Some of these partitions have clear biological interpretation; others can serve to identify possible directions for future research.

physics.bio-ph↗

arXiv1999

Super-paramagnetic clustering of yeast gene expression profiles

High-density DNA arrays, used to monitor gene expression at a genomic scale, have produced vast amounts of information which require the development of efficient computational methods to analyze them. The important first step is to extract the fundamental patterns of gene expression inherent in the data. This paper describes the application of a novel clustering algorithm, Super-Paramagnetic Clustering (SPC) to analysis of gene expression profiles that were generated recently during a study of the yeast cell cycle. SPC was used to organize genes into biologically relevant clusters that are suggestive for their co-regulation. Some of the advantages of SPC are its robustness against noise and initialization, a clear signature of cluster formation and splitting, and an unsupervised self-organized determination of the number of clusters at each resolution. Our analysis revealed interesting correlated behavior of several groups of genes which has not been previously identified.

physics.bio-ph↗