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Farhad Imam

Researcher at University of California, San Diego

Publications -  11
Citations -  1778

Farhad Imam is an academic researcher from University of California, San Diego. The author has contributed to research in topics: Gene & Induced pluripotent stem cell. The author has an hindex of 8, co-authored 11 publications receiving 1699 citations. Previous affiliations of Farhad Imam include Harvard University & Stanford University.

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Gene expression during the life cycle of Drosophila melanogaster.

TL;DR: These studies define major characteristics of the transcriptional programs that underlie the life cycle, compare development in males and females, and show that large-scale gene expression data collected from whole animals can be used to identify genes expressed in particular tissues and organs or genes involved in specific biological and biochemical processes.
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Chromatin signature of embryonic pluripotency is established during genome activation

TL;DR: In this paper, the genomic locations of histone H3 molecules bearing lysine trimethylation modifications before and after the maternal-zygotic transition in zebrafish were mapped to study the changes in chromatin structure that accompany pluripotency and genome activation.
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Drosophila Ecdysone Receptor Mutations Reveal Functional Differences among Receptor Isoforms

TL;DR: Two classes of EcR mutations are identified and molecularly mapped: those specific to EcR-B1 that uncouple metamorphosis, and embryonic-lethal mutations that map to common sequences encoding the DNA- and ligand-binding domains.
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stumps, a Drosophila gene required for fibroblast growth factor (FGF)-directed migrations of tracheal and mesodermal cells.

TL;DR: The results suggest that stumps function promotes FGF-directed cell migrations, either by potentiating the FGF signaling process or by coupling the signal to the cellular machinery required for directed cell movement.
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The computational analysis of scientific literature to define and recognize gene expression clusters

TL;DR: A computational method is presented that leverages the peer-reviewed literature in the automatic analysis of gene expression data sets with different properties and is able to rapidly define and identify the biologically relevant gene expression profiles without manual intervention.