Publications by authors named "D D Tritchler"

Recruitment of minority participants to clinical trials, especially studies without therapeutic intent, has been historically challenging. This study describes barriers to and successes of recruitment and retention strategies to dietary studies. A flaxseed study was conducted in healthy, postmenopausal women of African ancestry (AA) and European ancestry (EA) to assess associations between gut microbial community composition and host metabolism (NCT01698294).

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Motivation: High-dimensional genomic data can be analyzed to understand the effects of variables on a target variable such as a clinical outcome. For understanding the underlying biological mechanism affecting the target, it is important to discover the complete set of relevant variables. Thus variable selection is a primary goal, which differs from a prediction criterion.

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Multi-omic analyses that integrate many high-dimensional datasets often present significant deficiencies in statistical power and require time consuming computations to execute the analytical methods. We present SuMO-Fil to remedy against these issues which is a pre-processing method for Supervised Multi-Omic Filtering that removes variables or features considered to be irrelevant noise. SuMO-Fil is intended to be performed prior to downstream analyses that detect supervised gene networks in sparse settings.

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Lignans are phytochemicals studied extensively as dietary factors in chronic disease etiology. Our goal was to examine associations between the gut microbiota and lignan metabolism and whether these associations differ by ethnicity. We conducted a flaxseed (FS) dietary intervention in 252 healthy, postmenopausal women of African ancestry (AA) and European ancestry (EA).

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Functional pathways involve a series of biological alterations that may result in the occurrence of many diseases including cancer. With the availability of various "omics" technologies it becomes feasible to integrate information from a hierarchy of biological layers to provide a more comprehensive understanding to the disease. In many diseases, it is believed that only a small number of networks, each relatively small in size, drive the disease.

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