Gene Expression During the Life Cycle of Drosophila melanogaster

By M. Arbeitman, E. Furlong, Farhad B. Imam, Eric Johnson, Brian H. Null, B. S. Baker, M. Krasnow, M. Scott, R. W. Davis, Kevin P. White. Science, 2002

878 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1f63b5b73fec84b34d1d24ac30ef778e6cd2a1f5

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Citations in the corpus

Promoter features related to tissue specificity as measured by Shannon entropy (2005, methodology): “For microarray data, investigators have begun defining these patterns, largely through the application of clustering algorithms [14,15].”
Statistical inference of the time-varying structure of gene-regulation networks (2010, methodology): “The high number of changepoints at mid-embryogenesis (interval {18 to 19}) corresponds to a major morphological change related to a modification of transcriptional regulations, as described in [28].”
A state-space mixed membership blockmodel for dynamic network tomography (2008, background): “ be observed for the gene regulatory networks of Drosophila melanogaster. We reverse engineered the sequence of gene regulatory networks based on a time series of microarray measuremetns collected by Arbeitman et al. (2002). In the microarray experiments, the expression levels of 4028 genes are simu”
PAT proteins, an ancient family of lipid droplet proteins that regulate cellular lipid stores (2009, background): “LSD-2 promotes storage of neutral lipids: Like LSD-1, Drosophila LSD-2 is expressed in an intricate tissue-specific pattern consistent with roles in lipid metabolism [28, 170,172,180].”
Functional clustering and identifying substructures of longitudinal data (2007, methodology): “The Drosophilia life cycle gene expression profile data (Arbeitman et al., 2002) are used as the second example.”
Direct induction of autophagy by Atg1 inhibits cell growth and induces apoptotic cell death. (2007, background): “In addition, the level of Atg1 gene expression may be critical; it is not upregulated under starvation conditions [27], but its levels peak at the beginning of the Drosophila pupal stage [42], when autophagic cell death is induced.”
Functional Additive Models (2008, methodology): “Arbeitman et al. (2002) obtained developmental gene expression profiles over the entire lifespan of Drosophila, and we applied functional regression to study the relation of the expression profiles for different developmental periods (see also Müller, Chiou, and Leng 2008).”
Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality (2012, methodology): “Causal Inference of Gene Networks : We use time-course gene expression microarray data measured during the full life cycle of Drosophila melanogaster [2].”
Ultrasensitive proteome analysis using paramagnetic bead technology (2014, positive): “Shifts in candidate expression associated with the MZT based on transcript (Arbeitman et al , 2002) or protein (Gouw et al , 2009) data were easily distinguishable at the single-embryo level between stages.”
Population Genomics: Whole-Genome Analysis of Polymorphism and Divergence in Drosophila simulans (2007, neutral): “[139].”
Inferring gene regulatory networks from time series data using the minimum description length principle (2006, positive): “When applied on real Drosophila time-series datasets, the proposed network inference algorithm corroborates some of the ﬁndings of Arbeitman et al .”
Recovering time-varying networks of dependencies in social and biological studies (2009, background): “(34) in their study of the gene expression patterns during the life cycle of D.”
Global analysis of mRNA localization reveals a prominent role in organizing cellular architecture and function. (2007, result): “%) is more than twice that of earlier predictions ( 30%; Arbeitman et al., 2002), although it more closely resembles estimates from a recent microarray-based study ( 55%; Tadros et al., 2007a).”
Information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments (2009, neutral): “Examples of such studies include response to temperature changes and other stress conditions [4], immune response [5], developmental studies [6], and various systems in the cell [7].”
STEM: a tool for the analysis of short time series gene expression data (2006, background): “Microarray time series gene expression experiments are widely used to study a range of biological processes such as the cell cycle [1], development [2], and immune response [3].”