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How Should Species Phylogenies Be Inferred from
How Should Species Phylogenies Be Inferred from

... set of sequences representing two different loci for a series of species, where “loci” is used very loosely to refer to any two disjoint segments of DNA and each locus by itself is assumed to define a linkage partition. There are several possible situations: (1) The loci fall on different chromosome ...
Further Topics in Optimization
Further Topics in Optimization

... Randomly alters each gene with a small probability (generally not greater than 0.01) ...
MetaXcan: Summary Statistics Based Gene-Level
MetaXcan: Summary Statistics Based Gene-Level

... are imputed with models trained in measured transcriptome datasets (e.g. GTEx). These predicted expression levels are then correlated with the phenotype and provides the basis for a gene-level association test that addresses some of the key limitations of GWAS [9]. Other groups have also proposed me ...
PDF
PDF

... nt+8d - . That is, the distinctions blurred by the abstraction n do not effect the probability of the evidence. We can always find a safe abstraction, since the trivial abstraction that consists of singletons is always safe. Moreover, it is clear that there exist a maximally safe abstraction which i ...
Parasitism and Mutualism in Wolbachia: What the
Parasitism and Mutualism in Wolbachia: What the

... Ecological and evolutionary theories predict that parasitism and mutualism are not fixed endpoints of the symbiotic spectrum. Rather, parasitism and mutualism may be host or environment dependent, induced by the same genetic machinery, and shifted due to selection. These models presume the existence ...
X - Bioinformatics.ca
X - Bioinformatics.ca

... There are other resampling (e.g. Dudoit and Fridlyand, 2002) and non-resampling based rules for estimating the number of clusters (for review see Milligan and Cooper (1978) and Dudoit and Fridlyand (2002) ). The bottom line is that none work very well in complicated situation and, to a large extent, ...
graphBAM and MultiGraph classes.
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A Mechanistic Beta-Binomial Probability Model for mRNA
A Mechanistic Beta-Binomial Probability Model for mRNA

... Copyright: © 2016 Smith, Birtwistle. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ...
Van de Mark, Daniel: The Numerous Caveats of Designing, Implementing, and Interpreting Genome-Wide Association Studies
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Review Slides

... sparse in that proportion of mutation is small compared to the whole genome size and they are heterogeneous in that there are several types of mutation possible for a given loci in a genome. It has been observed that in several cases patients with same clinical profiles do not share even a single mu ...
The emergence of individual species
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A process for analysis of microarray comparative genomics
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Bayesian Networks Classifiers for Gene-Expression Data
Bayesian Networks Classifiers for Gene-Expression Data

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The Application of Genetic Engineering in Forestry

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A machine learning approach to gene expression data analysis
A machine learning approach to gene expression data analysis

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Phylogenetic Motif Detection by Expectation
Phylogenetic Motif Detection by Expectation

... statistically enriched. Another method, FootPrinter, [8] identifies sequences (with mismatches) with few changes over an evolutionary tree. Neither of these methods, however, makes use of an explicit probabilistic model. Here we present a unified probabilistic framework that combines the mixture mod ...
Genotyping errors - Proceedings of the Royal Society B
Genotyping errors - Proceedings of the Royal Society B

... replicates. Thus, ef assumes that genotyping errors are essentially due to fragment dropouts and is less conservative because n does not include recessive phenotypes without mismatches. This assumption appears plausible since false bands at a truly recessive phenotype position must be rare given the ...
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Quantitative comparative linguistics

Statistical methods have been used in comparative linguistics since at least the 1950s (see Swadesh list). Since about the year 2000, there has been a renewed interest in the topic, based on the application of methods of computational phylogenetics and cladistics to define an optimal tree (or network) to represent a hypothesis about the evolutionary ancestry and perhaps its language contacts. The probability of relatedness of languages can be quantified and sometimes the proto-languages can be approximately dated.The topic came the attention of the popular press in 2003 after the publication of a short study on Indo-European in Nature (Gray and Atkinson 2003). A volume of articles on Phylogenetic Methods and the Prehistory of Languages was published in 2006 as the result of a conference held in Cambridge in 2004.A goal of comparative historical linguistics is to identify instances of genetic relatedness amongst languages. The steps in quantitative analysis are (i) to devise a procedure based on theoretical grounds, on a particular model or on past experience, etc. (ii) to verify the procedure by applying it to some data where there exists a large body of linguistic opinion for comparison (this may lead to a revision of the procedure of stage (i) or at the extreme of its total abandonment) (iii) to apply the procedure to data where linguistic opinions have not yet been produced, have not yet been firmly established or perhaps are even in conflict.Applying phylogenetic methods to languages is a multi-stage process (a) the encoding stage - getting from real languages to some expression of the relationships between them in the form of numerical or state data, so that those data can then be used as input to phylogenetic methods (b) the representation stage - applying phylogenetic methods to extract from those numerical and/or state data a signal that is converted into some useful form of representation, usually two dimensional graphical ones such as trees or networks, which synthesise and ""collapse"" what are often highly complex multi dimensional relationships in the signal (c) the interpretation stage - assessing those tree and network representations to extract from them what they actually mean for real languages and their relationships through time.
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