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The soybean aphid (Aphis glycines) is a major phloem-feeding pest of soybean (Glycine max). A. glycines feeding can cause the diversion of photosynthates and transmission of plant viruses, resulting in significant yield losses. In this study, we used oligonucleotide microarrays to characterize the long-term transcriptional response to soybean aphid(More)
Statistical regularisation methods such as LASSO and related L1 regularised regression methods are commonly used to construct models of gene regulatory networks. Although they can theoretically infer the correct network structure, they have been shown in practice to make errors, i.e. leave out existing links and include non-existing links. We show that L1(More)
MOTIVATION Gene regulatory network (GRN) inference reveals the influences genes have on one another in cellular regulatory systems. If the experimental data are inadequate for reliable inference of the network, informative priors have been shown to improve the accuracy of inferences. RESULTS This study explores the potential of undirected,(More)
Phytohormones mediate plant defense responses to pests and pathogens. In particular, the hormones jasmonic acid, ethylene, salicylic acid, and abscisic acid have been shown to dictate and fine-tune defense responses, and identification of the phytohormone components of a particular defense response is commonly used to characterize it. Identification of(More)
Gene regulatory network inference (that is, determination of the regulatory interactions between a set of genes) provides mechanistic insights of central importance to research in systems biology. Most contemporary network inference methods rely on a sparsity/regularization coefficient, which we call ζ (zeta), to determine the degree of sparsity of the(More)
A key question in network inference, that has not been properly answered, is what accuracy can be expected for a given biological dataset and inference method. We present GeneSPIDER - a Matlab package for tuning, running, and evaluating inference algorithms that allows independent control of network and data properties to enable data-driven benchmarking.(More)
The soybean aphid (Aphis glycines) is one of the main insect pests of soybean (Glycine max) worldwide. Genomics approaches have provided important data on transcriptome changes, both in the insect and in the plant, in response to the plant-aphid interaction. However, the difficulties to transform soybean and to rear soybean aphid on artificial media have(More)
resistance gene Rag1 does not protect against soybean cyst and root-knot nematodes. The soybean cyst nematode (SCN), Heterodera glycines, is the main soybean (Glycine max) pest in the United States (3); in addition, root-knot nematodes (RKN), Meloidogyne spp., also cause substantial yield losses. Currently, the most feasible option for nematode management(More)
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