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Copy number variation (CNV) is a phenomenon in which sections of the genome are repeated and the number of repeats in the genome varies between individuals. [1] Copy number variation is a type of structural variation: specifically, it is a type of duplication or deletion event that affects a considerable number of base pairs. [2]
Copy number analysis is the process of analyzing data produced by a test for DNA copy number variation in an organism's sample. One application of such analysis is the detection of chromosomal copy number variation that may cause or may increase risks of various critical disorders.
Gains: A copy number gain represents the gain of genetic material. If the gain is of just one additional copy of a segment of DNA, it may be called a duplication (Fig 4). If there is one extra copy of an entire chromosome, it may be called a trisomy. Copy number gains in germline samples may be disease-associated or may be a benign copy number ...
Homogeneously staining regions (HSRs) are chromosomal segments with various lengths and uniform staining intensity after G banding.This type of aberration is also known as Copy Number Gains or Amplification.
Array CGH has proven to be a specific, sensitive, fast and high-throughput technique, with considerable advantages compared to other methods used for the analysis of DNA copy number changes making it more amenable to diagnostic applications. Using this method, copy number changes at a level of 5–10 kilobases of DNA sequences can be detected. [15]
Multiplex ligation-dependent probe amplification (MLPA) is a variation of the multiplex polymerase chain reaction that permits amplification of multiple targets with only a single primer pair. [1] It detects copy number changes at the molecular level, and software programs are used for analysis.
Although information gain is usually a good measure for deciding the relevance of an attribute, it is not perfect. A notable problem occurs when information gain is applied to attributes that can take on a large number of distinct values. For example, suppose that one is building a decision tree for some data describing the customers of a business.
One of the applications of locally decodable codes in complexity theory is hardness amplification. Using LDCs with polynomial codeword length and polylogarithmic query complexity, one can take a function : {,} {,} that is hard to solve on worst case inputs and design a function ′: {,} {,} that is hard to compute on average case inputs.