829 research outputs found
Testing the Accuracy of Eukaryotic Phylogenetic Profiles for Prediction of Biological Function
A phylogenetic profile captures the pattern of gene gain and loss throughout evolutionary time. Proteins that interact directly or indirectly within the cell to perform a biological function will often co-evolve, and this co-evolution should be well reflected within their phylogenetic profiles. Thus similar phylogenetic profiles are commonly used for grouping proteins into functional groups. However, it remains unclear how the size and content of the phylogenetic profile impacts the ability to predict function, particularly in Eukaryotes. Here we developed a straightforward approach to address this question by constructing a complete set of phylogenetic profiles for 31 fully sequenced Eukaryotes. Using Gene Ontology as our gold standard, we compared the accuracy of functional predictions made by a comprehensive array of permutations on the complete set of genomes. Our permutations showed that phylogenetic profiles containing between 25 and 31 Eukaryotic genomes performed equally well and significantly better than all other permuted genome sets, with one exception: we uncovered a core of group of 18 genomes that achieved statistically identical accuracy. This core group contained genomes from each branch of the eukaryotic phylogeny, but also contained several groups of closely related organisms, suggesting that a balance between phylogenetic breadth and depth may improve our ability to use Eukaryotic specific phylogenetic profiles for functional annotations
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Genetic Networks of Complex Disorders: from a Novel Search Engine for PubMed Article Database
Finding genetic risk factors of complex disorders may involve reviewing hundreds of genes or thousands of research articles iteratively, but few tools have been available to facilitate this procedure. In this work, we built a novel publication search engine that can identify target-disorder specific, genetics-oriented research articles and extract the genes with significant results. Preliminary test results showed that the output of this engine has better coverage in terms of genes or publications, than other existing applications. We consider it as an essential tool for understanding genetic networks of complex disorders
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The future of genomics in pathology
The recent advances in technology and the promise of cheap and fast whole genomic data offer the possibility to revolutionise the discipline of pathology. This should allow pathologists in the near future to diagnose disease rapidly and early to change its course, and to tailor treatment programs to the individual. This review outlines some of these technical advances and the changes needed to make this revolution a reality
Can we accelerate autism discoveries through crowdsourcing?
AbstractAutism is a dramatically expanding public health challenge. The search for genomic variants underlying the disease concomitantly accelerated over the last 5 years, leading to a general consensus that genetics can explain between 40% and 60% of the symptomatic variability seen in autism. This stresses both an urgent need to continue devoting resources to the search for genetic etiologies that define the forms of autism, and an equal need for attention to the interactive roles of the environment. While some environmental factors have been investigated, few studies have attempted to elucidate the combination and interplay between gene and environment to gain clear understanding of the mechanisms by which environmental factors interact with genetic susceptibilities in Autism Spectrum Disorder. Due to financial constraints as well as recruitment protocols limited by geography, such studies have been challenging to implement. We discuss here how crowdsourcing approaches can overcome these limitations
A simple dependence between protein evolution rate and the number of protein-protein interactions
BACKGROUND: It has been shown for an evolutionarily distant genomic comparison that the number of protein-protein interactions a protein has correlates negatively with their rates of evolution. However, the generality of this observation has recently been challenged. Here we examine the problem using protein-protein interaction data from the yeast Saccharomyces cerevisiae and genome sequences from two other yeast species. RESULTS: In contrast to a previous study that used an incomplete set of protein-protein interactions, we observed a highly significant correlation between number of interactions and evolutionary distance to either Candida albicans or Schizosaccharomyces pombe. This study differs from the previous one in that it includes all known protein interactions from S. cerevisiae, and a larger set of protein evolutionary rates. In both evolutionary comparisons, a simple monotonic relationship was found across the entire range of the number of protein-protein interactions. In agreement with our earlier findings, this relationship cannot be explained by the fact that proteins with many interactions tend to be important to yeast. The generality of these correlations in other kingdoms of life unfortunately cannot be addressed at this time, due to the incompleteness of protein-protein interaction data from organisms other than S. cerevisiae. CONCLUSIONS: Protein-protein interactions tend to slow the rate at which proteins evolve. This may be due to structural constraints that must be met to maintain interactions, but more work is needed to definitively establish the mechanism(s) behind the correlations we have observed
TempT: Temporal consistency for Test-time adaptation
We introduce Temporal consistency for Test-time adaptation (TempT) a novel
method for test-time adaptation on videos through the use of temporal coherence
of predictions across sequential frames as a self-supervision signal. TempT is
an approach with broad potential applications in computer vision tasks
including facial expression recognition (FER) in videos. We evaluate TempT
performance on the AffWild2 dataset. Our approach focuses solely on the
unimodal visual aspect of the data and utilizes a popular 2D CNN backbone in
contrast to larger sequential or attention-based models used in other
approaches. Our preliminary experimental results demonstrate that TempT has
competitive performance compared to the previous years reported performances
and its efficacy provides a compelling proof-of-concept for its use in various
real-world applications.Comment: 7 Pages, 3 figure
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