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Amir Foroushani

Researcher at National Institutes of Health

Publications -  14
Citations -  1394

Amir Foroushani is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Endogenous retrovirus & Innate immune system. The author has an hindex of 8, co-authored 14 publications receiving 1085 citations. Previous affiliations of Amir Foroushani include Teagasc & University of British Columbia.

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InnateDB: systems biology of innate immunity and beyond—recent updates and continuing curation

TL;DR: The recent integration of bovine data makes InnateDB the first integrated network analysis platform for this agriculturally important model organism, and a range of improvements to the integrated bioinformatics solutions are reported.
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Next generation sequencing reveals the expression of a unique miRNA profile in response to a gram-positive bacterial infection.

TL;DR: A next generation sequencing approach profiling the expression of miRNAs in primary bovine mammary epithelial cells at 1, 2, 4 and 6 hours post-infection with Streptococcus uberis suggests that mi RNAs, which potentially act as central regulators of gene expression responses to a Gram-positive bacterial infection, may significantly regulate the sentinel capacity of mammaries to mobilise the innate immune system.
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Curating the innate immunity interactome

TL;DR: Curation of the InnateDB interactome provides a wealth of information to enable systems-level analysis of innate immunity and provides several lines of evidence that analysis of the innate immunity interactome has the potential to identify novel signalling, transcriptional and post-transcriptional regulators of innate Immunity.
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Enhancement of LIN28B-induced hematopoietic reprogramming by IGF2BP3.

TL;DR: Observations indicate that Lin28b–Igf2bp3 are developmental regulators that mediate the fetal–adult hematopoietic switch.
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Applications of Bayesian Network Models in Predicting Types of Hematological Malignancies

TL;DR: A novel method based on the analyses of coexpression networks and Bayesian networks is introduced, and this new method is used to classify two types of hematological malignancies; namely, acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS).