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Nuclear Profile
In molecular biology, genome architecture mapping (GAM) is a cryosectioning method to Gene mapping, map Colocalization, colocalized DNA regions in a Ligation (molecular biology), ligation independent manner. It overcomes some limitations of Chromosome conformation capture (3C), as these methods have a reliance on digestion and Ligation (molecular biology), ligation to capture interacting DNA segments. GAM is the first genome-wide method for capturing three-dimensional proximities between any number of Locus (genetics), genomic loci without ligation. The sections that are found using the cryosectioning method mentioned above are referred to as nuclear profiles. The information that they provide relates to their coverage across a genome. A large set of values can be produced that represents the strength of nuclear profiles’ presence within a genome. Based on how large or small the coverage across a genome is, judgements can be made involving chromatin interactions, nuclear profile l ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Molecular Biology
Molecular biology is a branch of biology that seeks to understand the molecule, molecular basis of biological activity in and between Cell (biology), cells, including biomolecule, biomolecular synthesis, modification, mechanisms, and interactions. Though cells and other microscopic structures had been observed in living organisms as early as the 18th century, a detailed understanding of the mechanisms and interactions governing their behavior did not emerge until the 20th century, when technologies used in physics and chemistry had advanced sufficiently to permit their application in the biological sciences. The term 'molecular biology' was first used in 1945 by the English physicist William Astbury, who described it as an approach focused on discerning the underpinnings of biological phenomena—i.e. uncovering the physical and chemical structures and properties of biological molecules, as well as their interactions with other molecules and how these interactions explain observ ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cosegregation
Cosegregation, in genealogy, refers to the tendency of two or more genes located close together on the same chromosome to be inherited together during cell division. Due to their physical proximity, these genes are considered genetically linked and are likely to be inherited together. In genetics, the term may also refer to the estimated probability of interaction between multiple loci or specific regions within a target gene. This probability is assessed using data derived from nuclear profiles (NPs), which are thin slices taken from a cell nucleus. Within each NP, the presence or absence of particular loci is evaluated. These interaction probabilities—referred to as cosegregation values—are used in mathematical models such as SLICE and normalized linkage disequilibrium. These models contribute to the generation of 3D genome architecture maps as part of genome architecture mapping (GAM) techniques. The resulting 3D renderings provide insights into genomic density and the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Detection Efficiency Flowchart
{{Unreferenced, date=March 2018 In general, detection is the action of accessing information without specific cooperation from with the sender. In the history of radio communications, the term "detector" was first used for a device that detected the simple presence or absence of a radio signal, since all communications were in Morse code. The term is still in use today to describe a component that extracts a particular signal from all of the electromagnetic waves present. Detection is usually based on the frequency of the carrier signal, as in the familiar frequencies of radio broadcasting, but it may also involve filtering a faint signal from noise, as in radio astronomy, or reconstructing a hidden signal, as in steganography. In optoelectronics, "detection" means converting a received optical input to an electrical output. For example, the light signal received through an optical fiber is converted to an electrical signal in a detector such as a photodiode. In steganography, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Monte Carlo Method
Monte Carlo methods, or Monte Carlo experiments, are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. The underlying concept is to use randomness to solve problems that might be deterministic in principle. The name comes from the Monte Carlo Casino in Monaco, where the primary developer of the method, mathematician Stanisław Ulam, was inspired by his uncle's gambling habits. Monte Carlo methods are mainly used in three distinct problem classes: optimization, numerical integration, and generating draws from a probability distribution. They can also be used to model phenomena with significant uncertainty in inputs, such as calculating the risk of a nuclear power plant failure. Monte Carlo methods are often implemented using computer simulations, and they can provide approximate solutions to problems that are otherwise intractable or too complex to analyze mathematically. Monte Carlo methods are widely used in va ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Flowchart Of SLICE
A flowchart is a type of diagram that represents a workflow or process. A flowchart can also be defined as a diagrammatic representation of an algorithm, a step-by-step approach to solving a task. The flowchart shows the steps as boxes of various kinds, and their order by connecting the boxes with arrows. This diagrammatic representation illustrates a solution model to a given problem. Flowcharts are used in analyzing, designing, documenting or managing a process or program in various fields. * ''Document flowcharts'', showing controls over a document-flow through a system * ''Data flowcharts'', showing controls over a data-flow in a system * ''System flowcharts'', showing controls at a physical or resource level * ''Program flowchart'', showing the controls in a program within a system Notice that every type of flowchart focuses on some kind of control, rather than on the particular flow itself. However, there are some different classifications. For example, Andrew Veronis ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Eigenvector Centrality
In graph theory, eigenvector centrality (also called eigencentrality or prestige score) is a measure of the influence of a node in a connected network. Relative scores are assigned to all nodes in the network based on the concept that connections to high-scoring nodes contribute more to the score of the node in question than equal connections to low-scoring nodes. A high eigenvector score means that a node is connected to many nodes who themselves have high scores. Google's PageRank and the Katz centrality are variants of the eigenvector centrality. Using the adjacency matrix to find eigenvector centrality For a given graph G:=(V,E) with , V, vertices let A = (a_) be the adjacency matrix, i.e. a_ = 1 if vertex v is linked to vertex t, and a_ = 0 otherwise. The relative centrality score, x_v, of vertex v can be defined as: : x_v = \frac 1 \lambda \sum_ x_t = \frac 1 \lambda \sum_ a_ x_t where M(v) is the set of neighbors of v and \lambda is a constant. With a small rearrange ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Centrality
In graph theory and network analysis, indicators of centrality assign numbers or rankings to nodes within a graph corresponding to their network position. Applications include identifying the most influential person(s) in a social network, key infrastructure nodes in the Internet or urban networks, super-spreaders of disease, and brain networks. Centrality concepts were first developed in social network analysis, and many of the terms used to measure centrality reflect their sociological origin.Newman, M.E.J. 2010. ''Networks: An Introduction.'' Oxford, UK: Oxford University Press. Definition and characterization of centrality indices Centrality indices are answers to the question "What characterizes an important vertex?" The answer is given in terms of a real-valued function on the vertices of a graph, where the values produced are expected to provide a ranking which identifies the most important nodes. The word "importance" has a wide number of meanings, leading to many d ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Clique Problem
In computer science, the clique problem is the computational problem of finding cliques (subsets of vertices, all adjacent to each other, also called complete subgraphs) in a graph. It has several different formulations depending on which cliques, and what information about the cliques, should be found. Common formulations of the clique problem include finding a maximum clique (a clique with the largest possible number of vertices), finding a maximum weight clique in a weighted graph, listing all maximal cliques (cliques that cannot be enlarged), and solving the decision problem of testing whether a graph contains a clique larger than a given size. The clique problem arises in the following real-world setting. Consider a social network, where the graph's vertices represent people, and the graph's edges represent mutual acquaintance. Then a clique represents a subset of people who all know each other, and algorithms for finding cliques can be used to discover these groups ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
