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  2. Template modeling score - Wikipedia

    en.wikipedia.org/wiki/Template_modeling_score

    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 ]

  3. Structural alignment software - Wikipedia

    en.wikipedia.org/wiki/Structural_alignment_software

    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

  4. Root mean square deviation of atomic positions - Wikipedia

    en.wikipedia.org/wiki/Root_mean_square_deviation...

    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

  5. T-Coffee - Wikipedia

    en.wikipedia.org/wiki/T-Coffee

    T-Coffee (Tree-based Consistency Objective Function for Alignment Evaluation) is a multiple sequence alignment software using a progressive approach. [1] It generates a library of pairwise alignments to guide the multiple sequence alignment.

  6. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]

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  8. Scoring rule - Wikipedia

    en.wikipedia.org/wiki/Scoring_rule

    This same prediction also assigns 20% likelihood to the opposite case, and so if the prediction proves false, it would receive a score based on the 20%: ln(0.2) = −1.6. The goal of a forecaster is to maximize the score and for the score to be as large as possible, and −0.22 is indeed larger than −1.6.

  9. Lists of statistics topics - Wikipedia

    en.wikipedia.org/wiki/Lists_of_statistics_topics

    List of analyses of categorical data; List of fields of application of statistics; List of graphical methods; List of statistical software. Comparison of statistical packages; List of graphing software; Comparison of Gaussian process software; List of stochastic processes topics; List of matrices used in statistics; Timeline of probability and ...