T
Thomas J. Downey
Publications - 7
Citations - 1107
Thomas J. Downey is an academic researcher. The author has contributed to research in topics: Identity by descent & Gene. The author has an hindex of 7, co-authored 7 publications receiving 1072 citations.
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Journal ArticleDOI
Evaluation of gene expression measurements from commercial microarray platforms
Paul K. Tan,Thomas J. Downey,Edward L. Spitznagel,Pin Xu,Dadin Fu,Dimiter S. Dimitrov,Richard A. Lempicki,Bruce M. Raaka,Margaret C. Cam +8 more
TL;DR: Correlations in gene expression levels and comparisons for significant gene expression changes in this subset were calculated, and showed considerable divergence across the different platforms, suggesting the need for establishing industrial manufacturing standards, and further independent and thorough validation of the technology.
Journal ArticleDOI
Primary and secondary transcriptional effects in the developing human Down syndrome brain and heart
Rong Mao,Xiaowen Wang,Edward L. Spitznagel,Laurence P. Frelin,Jason C. Ting,Huashi Ding,Jung Whan Kim,Ingo Ruczinski,Thomas J. Downey,Jonathan Pevsner +9 more
TL;DR: In Down syndrome, there is a primary transcriptional effect of disruption of chromosome 21 gene expression, without a pervasive secondary effect on the remaining transcriptome, which suggests molecular changes that may underlie the Down syndrome phenotypes.
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Inference of relationships in population data using identity-by-descent and identity-by-state.
Eric L. Stevens,Greg Heckenberg,Elisha D.O. Roberson,Joseph D. Baugher,Thomas J. Downey,Jonathan Pevsner +5 more
TL;DR: This combined method allowed us to distinguish related individuals from those having atypical heterozygosity rates and determine which individuals were outliers with respect to their designated population, and further identified distant relatedness between individuals within populations.
Journal ArticleDOI
Applying artificial neural network models to clinical decision making.
Rumi Kato Price,Edward L. Spitznagel,Thomas J. Downey,Donald J. Meyer,Nathan K. Risk,Omar G. El-Ghazzawy +5 more
TL;DR: Artificial neural networks are introduced, flexible nonlinear modeling techniques that test a model's generality by applying its estimates against "future" data and have potential for overcoming some shortcomings of linear models.
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Molecular diagnostics in sepsis : From bedside to bench
T. Philip Chung,Jason M. Laramie,Donald J. Meyer,Thomas J. Downey,Laurence H.Y. Tam,Huashi Ding,Timothy G. Buchman,Irene E. Karl,Gary D. Stormo,Richard S. Hotchkiss,J. Perren Cobb +10 more
TL;DR: Test the hypothesis that microarray expression profiles can be used to diagnose sepsis, distinguishing in vivo between sterile and infectious causes of systemic inflammation, and identified nine mouse common inflammatory response genes, six of which were mapped into a single pathway using contemporary pathway analysis tools.