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classical mds procedure  (MathWorks Inc)


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    MathWorks Inc classical mds procedure
    Classical <t>multidimensional</t> <t>scaling</t> <t>(MDS)</t> plot of various automatically generated partitions and the SCOP fold partition. The MDS plot approximately preserves distances, Δ, between partitions (see Methods). The X axis represents the projection of the location of each partition onto the first eigenvector (first principal component), while the Y axis is a projection onto the second eigenvector (second principal component). The X and Y axes of the plot are scaled to represent the distance, Δ, divided by 1000. Automatic partitions are obtained with Ward's method clustering, based on two different similarity score for each of the three methods VAST, SHEBA and DALI. Each partition is designated by an uppercase letter: A, VAST Pcli Level cut; B, VAST Nres Level cut; C, DALI Zscore Level cut; D, DALI Nres Level cut; E, SHEBA Zscore Level cut; F, SHEBA Nres Level cut; G, VAST Pcli Largest Size cut; H, VAST Nres Largest Size cut; I, DALI Zscore Largest Size cut; J, DALI Nres Largest Size cut; K, SHEBA Zscore Largest Size cut; L, SHEBA Nres Largest Size cut, and S, the expert-curated partition SCOP. Automatic partitions resulting from the same tree-cutting strategy, are grouped together within the same dotted area.
    Classical Mds Procedure, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    1) Product Images from "Towards an automatic classification of protein structural domains based on structural similarity"

    Article Title: Towards an automatic classification of protein structural domains based on structural similarity

    Journal: BMC Bioinformatics

    doi: 10.1186/1471-2105-9-74

    Classical multidimensional scaling (MDS) plot of various automatically generated partitions and the SCOP fold partition. The MDS plot approximately preserves distances, Δ, between partitions (see Methods). The X axis represents the projection of the location of each partition onto the first eigenvector (first principal component), while the Y axis is a projection onto the second eigenvector (second principal component). The X and Y axes of the plot are scaled to represent the distance, Δ, divided by 1000. Automatic partitions are obtained with Ward's method clustering, based on two different similarity score for each of the three methods VAST, SHEBA and DALI. Each partition is designated by an uppercase letter: A, VAST Pcli Level cut; B, VAST Nres Level cut; C, DALI Zscore Level cut; D, DALI Nres Level cut; E, SHEBA Zscore Level cut; F, SHEBA Nres Level cut; G, VAST Pcli Largest Size cut; H, VAST Nres Largest Size cut; I, DALI Zscore Largest Size cut; J, DALI Nres Largest Size cut; K, SHEBA Zscore Largest Size cut; L, SHEBA Nres Largest Size cut, and S, the expert-curated partition SCOP. Automatic partitions resulting from the same tree-cutting strategy, are grouped together within the same dotted area.
    Figure Legend Snippet: Classical multidimensional scaling (MDS) plot of various automatically generated partitions and the SCOP fold partition. The MDS plot approximately preserves distances, Δ, between partitions (see Methods). The X axis represents the projection of the location of each partition onto the first eigenvector (first principal component), while the Y axis is a projection onto the second eigenvector (second principal component). The X and Y axes of the plot are scaled to represent the distance, Δ, divided by 1000. Automatic partitions are obtained with Ward's method clustering, based on two different similarity score for each of the three methods VAST, SHEBA and DALI. Each partition is designated by an uppercase letter: A, VAST Pcli Level cut; B, VAST Nres Level cut; C, DALI Zscore Level cut; D, DALI Nres Level cut; E, SHEBA Zscore Level cut; F, SHEBA Nres Level cut; G, VAST Pcli Largest Size cut; H, VAST Nres Largest Size cut; I, DALI Zscore Largest Size cut; J, DALI Nres Largest Size cut; K, SHEBA Zscore Largest Size cut; L, SHEBA Nres Largest Size cut, and S, the expert-curated partition SCOP. Automatic partitions resulting from the same tree-cutting strategy, are grouped together within the same dotted area.

    Techniques Used: Generated



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    Classical <t>multidimensional</t> <t>scaling</t> <t>(MDS)</t> plot of various automatically generated partitions and the SCOP fold partition. The MDS plot approximately preserves distances, Δ, between partitions (see Methods). The X axis represents the projection of the location of each partition onto the first eigenvector (first principal component), while the Y axis is a projection onto the second eigenvector (second principal component). The X and Y axes of the plot are scaled to represent the distance, Δ, divided by 1000. Automatic partitions are obtained with Ward's method clustering, based on two different similarity score for each of the three methods VAST, SHEBA and DALI. Each partition is designated by an uppercase letter: A, VAST Pcli Level cut; B, VAST Nres Level cut; C, DALI Zscore Level cut; D, DALI Nres Level cut; E, SHEBA Zscore Level cut; F, SHEBA Nres Level cut; G, VAST Pcli Largest Size cut; H, VAST Nres Largest Size cut; I, DALI Zscore Largest Size cut; J, DALI Nres Largest Size cut; K, SHEBA Zscore Largest Size cut; L, SHEBA Nres Largest Size cut, and S, the expert-curated partition SCOP. Automatic partitions resulting from the same tree-cutting strategy, are grouped together within the same dotted area.
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    Classical multidimensional scaling (MDS) plot of various automatically generated partitions and the SCOP fold partition. The MDS plot approximately preserves distances, Δ, between partitions (see Methods). The X axis represents the projection of the location of each partition onto the first eigenvector (first principal component), while the Y axis is a projection onto the second eigenvector (second principal component). The X and Y axes of the plot are scaled to represent the distance, Δ, divided by 1000. Automatic partitions are obtained with Ward's method clustering, based on two different similarity score for each of the three methods VAST, SHEBA and DALI. Each partition is designated by an uppercase letter: A, VAST Pcli Level cut; B, VAST Nres Level cut; C, DALI Zscore Level cut; D, DALI Nres Level cut; E, SHEBA Zscore Level cut; F, SHEBA Nres Level cut; G, VAST Pcli Largest Size cut; H, VAST Nres Largest Size cut; I, DALI Zscore Largest Size cut; J, DALI Nres Largest Size cut; K, SHEBA Zscore Largest Size cut; L, SHEBA Nres Largest Size cut, and S, the expert-curated partition SCOP. Automatic partitions resulting from the same tree-cutting strategy, are grouped together within the same dotted area.

    Journal: BMC Bioinformatics

    Article Title: Towards an automatic classification of protein structural domains based on structural similarity

    doi: 10.1186/1471-2105-9-74

    Figure Lengend Snippet: Classical multidimensional scaling (MDS) plot of various automatically generated partitions and the SCOP fold partition. The MDS plot approximately preserves distances, Δ, between partitions (see Methods). The X axis represents the projection of the location of each partition onto the first eigenvector (first principal component), while the Y axis is a projection onto the second eigenvector (second principal component). The X and Y axes of the plot are scaled to represent the distance, Δ, divided by 1000. Automatic partitions are obtained with Ward's method clustering, based on two different similarity score for each of the three methods VAST, SHEBA and DALI. Each partition is designated by an uppercase letter: A, VAST Pcli Level cut; B, VAST Nres Level cut; C, DALI Zscore Level cut; D, DALI Nres Level cut; E, SHEBA Zscore Level cut; F, SHEBA Nres Level cut; G, VAST Pcli Largest Size cut; H, VAST Nres Largest Size cut; I, DALI Zscore Largest Size cut; J, DALI Nres Largest Size cut; K, SHEBA Zscore Largest Size cut; L, SHEBA Nres Largest Size cut, and S, the expert-curated partition SCOP. Automatic partitions resulting from the same tree-cutting strategy, are grouped together within the same dotted area.

    Article Snippet: The computation of pairwise Δ-distance between the 13 partitions resulted in a 13 by 13 matrix (see Table ) which is then used as input to the classical MDS procedure in Matlab [ ].

    Techniques: Generated