Jeffrey A. Thompson

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The human genome encodes for over 1800 microRNAs (miRNAs), which are short non-coding RNA molecules that function to regulate gene expression post-transcriptionally. Due to the potential for one miRNA to target multiple gene transcripts, miRNAs are recognized as a major mechanism to regulate gene expression and mRNA translation. Computational prediction of(More)
Increasing specific leaf weight (SLW) may improve leaf apparent photosynthesis (AP) in soybean [Glycine max (L.) Merr.] but screening for SLW and AP is laborious. The Objectives of this study were (i) to determine the time course of SLW and chlorophyll concentration in experimental lines selected for differences in SLW and (ii) to evaluate the potential use(More)
Large, publicly available gene expression datasets are often analyzed with the aid of machine learning algorithms. Although RNA-seq is increasingly the technology of choice, a wealth of expression data already exist in the form of microarray data. If machine learning models built from legacy data can be applied to RNA-seq data, larger, more diverse training(More)
Computational methods of inferring regions of noncoding DNA that regulate gene activity are important to efficient biological validation of gene regulatory control. In many cases the available resources may allow for relatively few biological assays to be performed, and computational results allow these assays to be tightly focused on the highest confidence(More)
Gene expression profiles quantify the expression of thousands of genes simultaneously, providing a snapshot in time of gene expression in a specific tissue. A gene expression profile can be helpful in understanding the association of genes to the progression of cancer and patient outcomes. However, these complex associations can be difficult to determine(More)
Many researchers now have available multiple high-dimensional molecular and clinical datasets when studying a disease. As we enter this multi-omic era of data analysis, new approaches that combine different levels of data (e.g. at the genomic and epigenomic levels) are required to fully capitalize on this opportunity. In this work, we outline a new approach(More)
Although most verified functional elements in non-coding DNA contain a highly conserved core region, this concept is not generally incorporated into de novo motif inference systems. In this work, we explore the utility of adding the notion of conserved core regions into a comparative genomics approach for the search for putative functional elements in(More)