5,502 research outputs found
Detection of regulator genes and eQTLs in gene networks
Genetic differences between individuals associated to quantitative phenotypic
traits, including disease states, are usually found in non-coding genomic
regions. These genetic variants are often also associated to differences in
expression levels of nearby genes (they are "expression quantitative trait
loci" or eQTLs for short) and presumably play a gene regulatory role, affecting
the status of molecular networks of interacting genes, proteins and
metabolites. Computational systems biology approaches to reconstruct causal
gene networks from large-scale omics data have therefore become essential to
understand the structure of networks controlled by eQTLs together with other
regulatory genes, and to generate detailed hypotheses about the molecular
mechanisms that lead from genotype to phenotype. Here we review the main
analytical methods and softwares to identify eQTLs and their associated genes,
to reconstruct co-expression networks and modules, to reconstruct causal
Bayesian gene and module networks, and to validate predicted networks in
silico.Comment: minor revision with typos corrected; review article; 24 pages, 2
figure
Unsupervised Learning for Robust Fitting:A Reinforcement Learning Approach
Robust model fitting is a core algorithm in a large number of computer vision
applications. Solving this problem efficiently for datasets highly contaminated
with outliers is, however, still challenging due to the underlying
computational complexity. Recent literature has focused on learning-based
algorithms. However, most approaches are supervised which require a large
amount of labelled training data. In this paper, we introduce a novel
unsupervised learning framework that learns to directly solve robust model
fitting. Unlike other methods, our work is agnostic to the underlying input
features, and can be easily generalized to a wide variety of LP-type problems
with quasi-convex residuals. We empirically show that our method outperforms
existing unsupervised learning approaches, and achieves competitive results
compared to traditional methods on several important computer vision problems.Comment: The preprint of paper accepted to CVPR 202
The Effect of Uncertainty on Contingent Valuation Estimates: A Comparison
We examine the impact of uncertainty on contingent valuation responses using (1) a survey of Canadian landowners about willingness to accept compensation for converting cropland to forestry and (2) a survey of Swedish residents about willingness to pay for forest conservation. Five approaches from the literature for incorporating respondent uncertainty are used and compared to the traditional RUM model with assumed certainty. The results indicate that incorporating uncertainty has the potential to increase fit, but could introduce additional variance. While some methods for uncertainty are an improvement over traditional approaches, we caution against systematic judgments about the effect of uncertainty on contingent valuation responses.respondent uncertainty, willingness to accept, contingent valuation
Joint assembly and genetic mapping of the Atlantic horseshoe crab genome reveals ancient whole genome duplication
Horseshoe crabs are marine arthropods with a fossil record extending back
approximately 450 million years. They exhibit remarkable morphological
stability over their long evolutionary history, retaining a number of ancestral
arthropod traits, and are often cited as examples of "living fossils." As
arthropods, they belong to the Ecdysozoa}, an ancient super-phylum whose
sequenced genomes (including insects and nematodes) have thus far shown more
divergence from the ancestral pattern of eumetazoan genome organization than
cnidarians, deuterostomes, and lophotrochozoans. However, much of ecdysozoan
diversity remains unrepresented in comparative genomic analyses. Here we use a
new strategy of combined de novo assembly and genetic mapping to examine the
chromosome-scale genome organization of the Atlantic horseshoe crab Limulus
polyphemus. We constructed a genetic linkage map of this 2.7 Gbp genome by
sequencing the nuclear DNA of 34 wild-collected, full-sibling embryos and their
parents at a mean redundancy of 1.1x per sample. The map includes 84,307
sequence markers and 5,775 candidate conserved protein coding genes. Comparison
to other metazoan genomes shows that the L. polyphemus genome preserves
ancestral bilaterian linkage groups, and that a common ancestor of modern
horseshoe crabs underwent one or more ancient whole genome duplications (WGDs)
~ 300 MYA, followed by extensive chromosome fusion
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