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Associate Professor of Genetics
Computational Genetics, Bioinformatics, Systems Biology, Genetics of Common
Human Diseases
Human health and disease is a complex adaptive system. As such, predicting
an individual's risk of a disease such as lung cancer or essential hypertension
is much like predicting what the weather will be like five days from now. As
with the weather, there will not be a single factor that determines an
individual's risk of disease. Instead, risk of disease is determined by many
genetic, genomic, proteomic, and environmental factors that interact in a
nonlinear manner in time and space. Thus, a successful research strategy for
identifying common disease risk factors must take this complexity into account.
We are pioneering a research strategy that embraces, rather than ignores, the
complexity of the genotype to phenotype mapping relationship. A central focus
of the lab is the development, evaluation, and application of novel
computational strategies for identifying combinations of genetic, genomic, and
proteomic risk factors that predict risk of common human disorders such as
cancer, cardiovascular diseases, and psychiatric diseases. Dissertation topics
include, but are not limited to, theoretical and/or applied studies in
bioinformatics, complex adaptive systems, computational genetics, data mining,
epidemiology, human genetics, microarray analysis, population genetics,
proteomics, statistical genetics, and systems biology.
Visit the Moore Lab website.
Publications
Asselbergs, F.W., Williams, S.M., Hebert, P.R., Coffey, C.S., Hillege, H.L.,
de Jong, P.E., Vaughan, D.E., van Gilst, W.H., Moore, J.H. Epistatic effects of
genes from the fibrinolytic, renin-angiotensin, and bradykinin systems on
plasma PAI-1 and t-PA levels. Genomics 89, 362-369 (2007).
Moore, J.H., Barney, N., Tsai, C.T., Chiang, F.T., Gui, J., White, B.C.
Symbolic modeling of epistasis. Human Heredity 63, 120-133 (2007).
Velez, D.R., White, B.C., Motsinger, A.A., Bush, W.S., Ritchie, M.D.,
Williams, S.M., Moore, J.H. A balanced accuracy function for epistasis modeling
in imbalanced datasets using multifactor dimensionality reduction. Genetic
Epidemiology 31, 306-315 (2007).
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