Search results for "Inference"
showing 10 items of 478 documents
A novel methodology for large-scale phylogeny partition
2011
Understanding the determinants of virus transmission is a fundamental step for effective design of screening and intervention strategies to control viral epidemics. Phylogenetic analysis can be a valid approach for the identification of transmission chains, and very-large data sets can be analysed through parallel computation. Here we propose and validate a new methodology for the partition of large-scale phylogenies and the inference of transmission clusters. This approach, on the basis of a depth-first search algorithm, conjugates the evaluation of node reliability, tree topology and patristic distance analysis. The method has been applied to identify transmission clusters of a phylogeny …
A Phylogenetic Analysis of Human Syntenies Revealed by Chromosome Painting in Euarchontoglires Orders
2010
To search for cytogenetic signatures that can help to clarify evolutionary affinities among the five orders within the Euarchontoglires clade, we focused on associations of conserved syntenic blocks that have been accumulated in the karyotypes of Primates (Strepsirhini and Haplorhini), five families of Rodentia, Scandentia (Tupaia belangeri), Dermoptera (Galeopterus variegatus) and Lagomorpha (Oryctolagus cuniculus). We examined available chromosome painting data to identify conserved chromosomes and chromosomal segments, and syntenic associations likely to have characterized the ancestral eutherian karyotype. The data set includes 161 characters that have been subjected to a concatenated a…
Phylogenetic signal and functional categories in Proteobacteria genomes
2007
Abstract Background A comprehensive evolutionary analysis of bacterial genomes implies to identify the hallmark of vertical and non-vertical signals and to discriminate them from the presence of mere phylogenetic noise. In this report we have addressed the impact of factors like the universal distribution of the genes, their essentiality or their functional role in the cell on the inference of vertical signal through phylogenomic methods. Results We have established that supermatrices derived from data sets composed mainly by genes suspected to be essential for bacterial cellular life perform better on the recovery of vertical signal than those composed by widely distributed genes. In addit…
Selective phenotyping, entropy reduction, and the mastermind game.
2011
Abstract Background With the advance of genome sequencing technologies, phenotyping, rather than genotyping, is becoming the most expensive task when mapping genetic traits. The need for efficient selective phenotyping strategies, i.e. methods to select a subset of genotyped individuals for phenotyping, therefore increases. Current methods have focused either on improving the detection of causative genetic variants or their precise genomic location separately. Results Here we recognize selective phenotyping as a Bayesian model discrimination problem and introduce SPARE (Selective Phenotyping Approach by Reduction of Entropy). Unlike previous methods, SPARE can integrate the information of p…
A Semantic Web Approach for Geodata Discovery
2013
International audience; Currently, vast amounts of geospatial information are o ffered through OGC's services. However this information has limited formal semantics. The most common method to search for a dataset consists in matching keywords to metadata elements. By adding semantics to available descriptions we could use modern inference and reasoning mechanisms currently available in the SemanticWeb. In this paper we present a novel architecture currently in development in which we use state of the art triplestores as the backend of a CSW service. In our approach, each metadata record is considered an instance of a given class in a domain ontology. Our architecture also adds a spatial dat…
Kernel-Based Inference of Functions Over Graphs
2018
Abstract The study of networks has witnessed an explosive growth over the past decades with several ground-breaking methods introduced. A particularly interesting—and prevalent in several fields of study—problem is that of inferring a function defined over the nodes of a network. This work presents a versatile kernel-based framework for tackling this inference problem that naturally subsumes and generalizes the reconstruction approaches put forth recently for the signal processing by the community studying graphs. Both the static and the dynamic settings are considered along with effective modeling approaches for addressing real-world problems. The analytical discussion herein is complement…
Robust Graph Topology Learning and Application in Stock Market Inference
2019
In many applications, there are multiple interacting entities, generating time series of data over the space. To describe the relation within the set of data, the underlying topology may be used. In many real applications, not only the signal/data of interest is measured in noise, but it is also contaminated with outliers. The proposed method, called RGTL, infers the graph topology from noisy measurements and removes these outliers simultaneously. Here, it is assumed that we have no information about the space graph topology, while we know that graph signal are sampled consecutively in time and thus the graph in time domain is given. The simulation results show that the proposed algorithm h…
Bayesian Survival Analysis to Model Plant Resistance and Tolerance to Virus Diseases
2017
Viruses constitute a major threat to large-scale production of crops worldwide producing important economical losses and undermining sustainability. We evaluated a new plant variety for resistance and tolerance to a specific virus through a comparison with other well-known varieties. The study is based on two independent Bayesian accelerated failure time models which assess resistance and tolerance survival times. Information concerning plant genotype and virus biotype were considered as baseline covariates and error terms were assumed to follow a modified standard Gumbel distribution. Frequentist approach to these models was also considered in order to compare the results of the study from…
When does Regression discontinuity design work? Evidence from random election outcomes
2018
We use elections data in which a large number of ties in vote counts between candidates are resolved via a lottery to study the personal incumbency advantage. We benchmark non‐experimental regression discontinuity design (RDD) estimates against the estimate produced by this experiment that takes place exactly at the cutoff. The experimental estimate suggests that there is no personal incumbency advantage. In contrast, conventional local polynomial RDD estimates suggest a moderate and statistically significant effect. Bias‐corrected RDD estimates that apply robust inference are, however, in line with the experimental estimate. Therefore, state‐of‐the‐art implementation of RDD can meet the re…
A recap on Linear Mixed Models and their hat-matrices
2017
This working paper has a twofold goal. On one hand, it provides a recap of Linear Mixed Models (LMMs): far from trying to be exhaustive, this first part of the working paper focusses on the derivation of theoretical results on estimation of LMMs that are scattered in the literature or whose mathematical derivation is sometimes missing or too quickly sketched. On the other hand, it discusses various definitions that are available in the literature for the hat-matrix of Linear Mixed Models, showing their limitations and proving their equivalence.