Shuffling Yeast Gene Expression Data

2000/06/20 by Sven Bilke, Bilke, Sven
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · #Bioinformatics and Genomic Networks #Biological Physics (physics.bio-ph) #Data Analysis #FOS: Biological sciences #FOS: Physical sciences #Gene Regulatory Network Analysis #Gene expression and cancer classification #Medical Physics (physics.med-ph) #Quantitative Methods (q-bio.QM) #Statistics and Probability (physics.data-an) #physics.bio-ph #physics.data-an #physics.med-ph #q-bio.QM

paper · pdf · doi:10.48550/arxiv.physics/0006050

8 pages, 2 figures. Submitted to Proceedings of the National Academy of Science USA

arxiv created 2000/06/20 · openalex publication_date 2000/06/20 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

A new method to sort gene expression patterns into functional groups is presented. The method is based on a sorting algorithm using a non-local similarity score, which takes all other patterns in the dataset into account. The method is therefore very robust with respect to noise. Using the expression data for yeast, we extract information about functional groups. Without prior knowledge of parameters the cell cycle regulated genes in yeast can be identified. Furthermore a second, independent cell clock is identified. The capability of the algorithm to extract information about signal flow in the regulatory network underlying the expression patterns is demonstrated.

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