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Deconvolving cell cycle expression data with complementary information
Author(s) -
Ziv BarJoseph,
S. Farkash,
David K. Gifford,
Itamar Simon,
Roni Rosenfeld
Publication year - 2004
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bth915
Subject(s) - expression (computer science) , computer science , data mining , computational biology , biology , programming language
In the study of many systems, cells are first synchronized so that a large population of cells exhibit similar behavior. While synchronization can usually be achieved for a short duration, after a while cells begin to lose their synchronization. Synchronization loss is a continuous process and so the observed value in a population of cells for a gene at time t is actually a convolution of its values in an interval around t. Deconvolving the observed values from a mixed population will allow us to obtain better models for these systems and to accurately detect the genes that participate in these systems.

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