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MCA is performed by applying the CA algorithm to either an indicator matrix (also called complete disjunctive table – CDT) or a Burt table formed from these variables. [citation needed] An indicator matrix is an individuals × variables matrix, where the rows represent individuals and the columns are dummy variables representing categories of the variables. [1]
A-DNA is thought to be one of three biologically active double helical structures along with B-DNA and Z-DNA. It is a right-handed double helix fairly similar to the more common B-DNA form, but with a shorter, more compact helical structure whose base pairs are not perpendicular to the helix-axis as in B-DNA.
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When data include both types of variables but the active variables being homogeneous, PCA or MCA can be used. Indeed, it is easy to include supplementary quantitative variables in MCA by the correlation coefficients between the variables and factors on individuals (a factor on individuals is the vector gathering the coordinates of individuals on a factorial axis); the representation obtained ...
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Non-B DNA can have significant implications for DNA biology and human health. For example, Z-DNA has been implicated in immunity and autoimmune diseases, such as lupus and arthritis. [6] H-DNA has been implicated in genomic instability and cancer, and G-quadruplexes have been linked to telomere maintenance, [7] oncogene activation, and cancer. [8]
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The structure a DNA molecule depends on its environment. In aqueous enviromnents, including the majority of DNA in a cell, B-DNA is the most common structure. The A-DNA structure is dominates in dehydrated samples and is similar to the double-stranded RNA and DNA/RNA hybrids. Z-DNA is a rarer structure found in DNA bound to certain proteins.