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Phylogenetic Analysis
In biology, phylogenetics (; from Greek φυλή/ φῦλον [] "tribe, clan, race", and wikt:γενετικός, γενετικός [] "origin, source, birth") is the study of the evolutionary history and relationships among or within groups of organisms. These relationships are determined by Computational phylogenetics, phylogenetic inference methods that focus on observed heritable traits, such as DNA sequences, protein amino acid sequences, or morphology. The result of such an analysis is a phylogenetic tree—a diagram containing a hypothesis of relationships that reflects the evolutionary history of a group of organisms. The tips of a phylogenetic tree can be living taxa or fossils, and represent the "end" or the present time in an evolutionary lineage. A phylogenetic diagram can be rooted or unrooted. A rooted tree diagram indicates the hypothetical common ancestor of the tree. An unrooted tree diagram (a network) makes no assumption about the ancestral line, and does n ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Biology
Biology is the scientific study of life. It is a natural science with a broad scope but has several unifying themes that tie it together as a single, coherent field. For instance, all organisms are made up of cells that process hereditary information encoded in genes, which can be transmitted to future generations. Another major theme is evolution, which explains the unity and diversity of life. Energy processing is also important to life as it allows organisms to move, grow, and reproduce. Finally, all organisms are able to regulate their own internal environments. Biologists are able to study life at multiple levels of organization, from the molecular biology of a cell to the anatomy and physiology of plants and animals, and evolution of populations.Based on definition from: Hence, there are multiple subdisciplines within biology, each defined by the nature of their research questions and the tools that they use. Like other scientists, biologists use the sc ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Phenetics
In biology, phenetics ( el, phainein – to appear) , also known as taximetrics, is an attempt to classify organisms based on overall similarity, usually in morphology or other observable traits, regardless of their phylogeny or evolutionary relation. It is closely related to numerical taxonomy which is concerned with the use of numerical methods for taxonomic classification. Many people contributed to the development of phenetics, but the most influential were Peter Sneath and Robert R. Sokal. Their books are still primary references for this sub-discipline, although now out of print. Phenetics has largely been superseded by cladistics for research into evolutionary relationships among species. However, certain phenetic methods, such as neighbor-joining, have found their way into phylogenetics, as a reasonable approximation of phylogeny when more advanced methods (such as Bayesian inference) are too computationally expensive. Phenetic techniques include various forms of cluste ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Morphology (biology)
Morphology is a branch of biology dealing with the study of the form and structure of organisms and their specific structural features. This includes aspects of the outward appearance (shape, structure, colour, pattern, size), i.e. external morphology (or eidonomy), as well as the form and structure of the internal parts like bones and organs, i.e. internal morphology (or anatomy). This is in contrast to physiology, which deals primarily with function. Morphology is a branch of life science dealing with the study of gross structure of an organism or taxon and its component parts. History The etymology of the word "morphology" is from the Ancient Greek (), meaning "form", and (), meaning "word, study, research". While the concept of form in biology, opposed to function, dates back to Aristotle (see Aristotle's biology), the field of morphology was developed by Johann Wolfgang von Goethe (1790) and independently by the German anatomist and physiologist Karl Friedrich Burdach ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Distance Matrix
In mathematics, computer science and especially graph theory, a distance matrix is a square matrix (two-dimensional array) containing the distances, taken pairwise, between the elements of a set. Depending upon the application involved, the ''distance'' being used to define this matrix may or may not be a metric. If there are elements, this matrix will have size . In graph-theoretic applications the elements are more often referred to as points, nodes or vertices. Non-metric distance matrix In general, a distance matrix is a weighted adjacency matrix of some graph. In a network, a directed graph with weights assigned to the arcs, the distance between two nodes of the network can be defined as the minimum of the sums of the weights on the shortest paths joining the two nodes. This distance function, while well defined, is not a metric. There need be no restrictions on the weights other than the need to be able to combine and compare them, so negative weights are used in some appli ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Phenetics
In biology, phenetics ( el, phainein – to appear) , also known as taximetrics, is an attempt to classify organisms based on overall similarity, usually in morphology or other observable traits, regardless of their phylogeny or evolutionary relation. It is closely related to numerical taxonomy which is concerned with the use of numerical methods for taxonomic classification. Many people contributed to the development of phenetics, but the most influential were Peter Sneath and Robert R. Sokal. Their books are still primary references for this sub-discipline, although now out of print. Phenetics has largely been superseded by cladistics for research into evolutionary relationships among species. However, certain phenetic methods, such as neighbor-joining, have found their way into phylogenetics, as a reasonable approximation of phylogeny when more advanced methods (such as Bayesian inference) are too computationally expensive. Phenetic techniques include various forms of cluste ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mathematical Model
A mathematical model is a description of a system using mathematical concepts and language. The process of developing a mathematical model is termed mathematical modeling. Mathematical models are used in the natural sciences (such as physics, biology, earth science, chemistry) and engineering disciplines (such as computer science, electrical engineering), as well as in non-physical systems such as the social sciences (such as economics, psychology, sociology, political science). The use of mathematical models to solve problems in business or military operations is a large part of the field of operations research. Mathematical models are also used in music, linguistics, and philosophy (for example, intensively in analytic philosophy). A model may help to explain a system and to study the effects of different components, and to make predictions about behavior. Elements of a mathematical model Mathematical models can take many forms, including dynamical systems, statisti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bayesian Inference
Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayesian inference is an important technique in statistics, and especially in mathematical statistics. Bayesian updating is particularly important in the dynamic analysis of a sequence of data. Bayesian inference has found application in a wide range of activities, including science, engineering, philosophy, medicine, sport, and law. In the philosophy of decision theory, Bayesian inference is closely related to subjective probability, often called "Bayesian probability". Introduction to Bayes' rule Formal explanation Bayesian inference derives the posterior probability as a consequence of two antecedents: a prior probability and a "likelihood function" derived from a statistical model for the observed data. Bayesian inference computes the posterior probability according to Bayes' theorem: ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Markov Chain Monte Carlo
In statistics, Markov chain Monte Carlo (MCMC) methods comprise a class of algorithms for sampling from a probability distribution. By constructing a Markov chain that has the desired distribution as its equilibrium distribution, one can obtain a sample of the desired distribution by recording states from the chain. The more steps that are included, the more closely the distribution of the sample matches the actual desired distribution. Various algorithms exist for constructing chains, including the Metropolis–Hastings algorithm. Application domains MCMC methods are primarily used for calculating numerical approximations of multi-dimensional integrals, for example in Bayesian statistics, computational physics, computational biology and computational linguistics. In Bayesian statistics, the recent development of MCMC methods has made it possible to compute large hierarchical models that require integrations over hundreds to thousands of unknown parameters. In rare even ... [...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 (phylogenetics)
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] |
Optimality Criterion
In statistics, an optimality criterion provides a measure of the fit of the data to a given hypothesis, to aid in model selection. A model is designated as the "best" of the candidate models if it gives the best value of an objective function measuring the degree of satisfaction of the criterion used to evaluate the alternative hypotheses. The term has been used to identify the different criteria that are used to evaluate a phylogenetic tree. For example, in order to determine the best topology between two phylogenetic trees using the maximum likelihood optimality criterion, one would calculate the maximum likelihood score of each tree and choose the one that had the better score. However, different optimality criteria can select different hypotheses. In such circumstances caution should be exercised when making strong conclusions. Many other disciplines use similar criteria or have specific measures geared toward the objectives of the field. Optimality criteria include maximum l ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Phylogenetic Inference
Computational phylogenetics is the application of computational algorithms, methods, and programs to phylogenetic"origin,_source,_birth")_is_the_study_of_the_evolutionary_his_... "origin, source, birth") is the study of the evolutionary his ... "origin, source, birth") is the study of the evolutionary his ... "origin, source, birth") is the study of the evolutionary his ... "origin, source, birth") is the study of the evolutionary his ... "tribe, clan, race", and wikt:γενετικός, γενετικός [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
