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The TM-score indicates the similarity between two structures by a score between (,], where 1 indicates a perfect match between two structures (thus the higher the better). [1] Generally scores below 0.20 corresponds to randomly chosen unrelated proteins whereas structures with a score higher than 0.5 assume roughly the same fold. [ 2 ]
TM-score – a different structure comparison measure; Longest continuous segment (LCS) — A different structure comparison measure; Global distance calculation (GDC_sc, GDC_all) — Structure comparison measures that use full-model information (not just α-carbon) to assess similarity
TM-align TM-score based protein structure alignment: Cα: Pair: nil: server and download: Y. Zhang & J. Skolnick: 2005 mTM-align Multiple protein structure alignment based on TM-align Cα Multi No server and download: R. Dong, Z. Peng, Y. Zhang & J. Yang 2018 VAST Vector Alignment Search Tool: SSE: Pair: nil: server: S. Bryant: 1996 PrISM
The quadratic scoring rule is a strictly proper scoring rule (,) = = =where is the probability assigned to the correct answer and is the number of classes.. The Brier score, originally proposed by Glenn W. Brier in 1950, [4] can be obtained by an affine transform from the quadratic scoring rule.
Transitive Consistency Score (TCS) is an extended version of the T-Coffee scoring scheme. [14] It uses T-Coffee libraries of pairwise alignments to evaluate any third party MSA. Pairwise projections can be produced using fast or slow methods, thus allowing a trade-off between speed and accuracy.
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The chart portion of the forest plot will be on the right hand side and will indicate the mean difference in effect between the test and control groups in the studies. A more precise rendering of the data shows up in number form in the text of each line, while a somewhat less precise graphic representation shows up in chart form on the right.
If the null hypothesis is true, the likelihood ratio test, the Wald test, and the Score test are asymptotically equivalent tests of hypotheses. [8] [9] When testing nested models, the statistics for each test then converge to a Chi-squared distribution with degrees of freedom equal to the difference in degrees of freedom in the two models. If ...