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Disk-covering Method
A disk-covering method is a divide-and-conquer meta-technique for large-scale phylogenetic analysis which has been shown to improve the performance of both heuristics for NP-hard optimization problems and polynomial-time distance-based methods. Disk-covering methods are a meta-technique in that they have flexibility in several areas, depending on the performance metrics that are being optimized for the base method. Such metrics can be efficiency, accuracy, or sequence length requirements for statistical performance. There have been several disk-covering methods developed, which have been applied to different "base methods". Disk-covering methods have been used with distance-based methods (like neighbor joining) to produce "fast-converging methods", which are methods that will reconstruct the true tree from sequences that have at most a polynomial number of sites. A disk-covering method has four steps: # Decomposition: Compute a decomposition of the dataset into overlapping subsets ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Neighbor Joining
In bioinformatics, neighbor joining is a bottom-up (agglomerative) clustering method for the creation of phylogenetic trees, created by Naruya Saitou and Masatoshi Nei in 1987. Usually based on DNA or protein sequence data, the algorithm requires knowledge of the distance between each pair of taxa (e.g., species or sequences) to create the phylogenetic tree. The algorithm Neighbor joining takes a distance matrix, which specifies the distance between each pair of taxa, as input. The algorithm starts with a completely unresolved tree, whose topology corresponds to that of a star network, and iterates over the following steps, until the tree is completely resolved, and all branch lengths are known: # Based on the current distance matrix, calculate a matrix Q (defined below). # Find the pair of distinct taxa i and j (i.e. with i \neq j) for which Q(i,j) is smallest. Make a new node that joins the taxa i and j, and connect the new node to the central node. For example, in part (B ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Tandy Warnow
Tandy Warnow is an American computer scientist and Grainger Distinguished Chair in Engineering at the University of Illinois at Urbana–Champaign.Tandy Warnow's , retrieved 2020-09-10. She is known for her work on the reconstruction of evolutionary trees, both in biology and in historical linguistics, and also for multiple sequence alignment methods. Biography Warnow did both her undergraduate and graduate studies in mathematics at the University of California, Berkeley, earning a bachelor's degree in 1984 and a PhD in 1991 under the supervision of Eugene Lawler. The other members of her dissertation committee were Richard Karp, Manuel Blum, Dan Gusfield, and David Gale. Research and career After postdoctoral research at the University of Southern California from 1991-1992 (postdoctoral supervisors Michael Waterman and Simon Tavare) and at Sandia National Laboratories in Albuquerque from 1992-1993, she took a faculty position at the University of Pennsylvania, where she rema ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bernard Moret
Bernard M. E. Moret (born 1953) is a Swiss-American computer scientist, an emeritus professor of Computer Science at the École Polytechnique Fédérale de Lausanne in Switzerland. He is known for his work in computational phylogenetics, and in particular for mathematics and methods for computing phylogenetic trees using genome rearrangement events. Biography Moret was born in 1953 in Vevey Switzerland, and did his undergraduate studies at the École Polytechnique Fédérale de Lausanne (EPFL), graduating in 1975. He went on to graduate studies at the University of Tennessee, earning a Ph.D. in 1980. He then joined the faculty of the University of New Mexico, where he remained until 2006, when he moved to EPFL.Curriculum vitae as of 2016, retrieved 2019-10-24. He retired from EPFL in December 2016. In 1996, Moret founded the '' [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Maximum Likelihood
In statistics, maximum likelihood estimation (MLE) is a method of estimation theory, estimating the Statistical parameter, parameters of an assumed probability distribution, given some observed data. This is achieved by Mathematical optimization, maximizing a likelihood function so that, under the assumed statistical model, the Realization (probability), observed data is most probable. The point estimate, point in the parameter space that maximizes the likelihood function is called the maximum likelihood estimate. The logic of maximum likelihood is both intuitive and flexible, and as such the method has become a dominant means of statistical inference. If the likelihood function is Differentiable function, differentiable, the derivative test for finding maxima can be applied. In some cases, the first-order conditions of the likelihood function can be solved analytically; for instance, the ordinary least squares estimator for a linear regression model maximizes the likelihood when ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Maximum Parsimony
In phylogenetics, maximum parsimony is an optimality criterion under which the phylogenetic tree that minimizes the total number of character-state changes (or miminizes the cost of differentially weighted character-state changes) is preferred. Under the maximum-parsimony criterion, the optimal tree will minimize the amount of homoplasy (i.e., convergent evolution, parallel evolution, and evolutionary reversals). In other words, under this criterion, the shortest possible tree that explains the data is considered best. Some of the basic ideas behind maximum parsimony were presented by James S. Farris in 1970 and Walter M. Fitch in 1971. Maximum parsimony is an intuitive and simple criterion, and it is popular for this reason. However, although it is easy to ''score'' a phylogenetic tree (by counting the number of character-state changes), there is no algorithm to quickly ''generate'' the most-parsimonious tree. Instead, the most-parsimonious tree must be sought in "tree space" ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
