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  2. Template modeling score - Wikipedia

    en.wikipedia.org/wiki/Template_modeling_score

    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] A quantitative study [3] shows that proteins of TM-score = 0.5 have a posterior probability of 37% in the same CATH topology family and of 13% in the same SCOP fold family. The ...

  3. Microsoft Excel - Wikipedia

    en.wikipedia.org/wiki/Microsoft_Excel

    Microsoft released the first version of Excel for the Macintosh on September 30, 1985, and the first Windows version was 2.05 (to synchronize with the Macintosh version 2.2) on November 19, 1987. [ 101 ] [ 102 ] Lotus was slow to bring 1-2-3 to Windows and by the early 1990s, Excel had started to outsell 1-2-3 and helped Microsoft achieve its ...

  4. Verbal Behavior Milestones Assessment and Placement Program

    en.wikipedia.org/wiki/Verbal_Behavior_Milestones...

    Barriers Assessment: Focuses on barriers that may impede the acquisition of new skills. Transition Assessment: Serves as a guide for planning the child's educational needs. Task Analysis and Skills Tracking: A checklist of skills that support the developmental milestones and can be used for daily curriculum activities and skill tracking.

  5. Talk:Template modeling score - Wikipedia

    en.wikipedia.org/wiki/Talk:Template_modeling_score

    Main page; Contents; Current events; Random article; About Wikipedia; Contact us; Pages for logged out editors learn more

  6. Millon Clinical Multiaxial Inventory - Wikipedia

    en.wikipedia.org/wiki/Millon_Clinical_Multiaxial...

    BR scores are indexed on a scale of 0 – 115, with 0 representing a raw score of 0, a score of 60 representing the median of a clinical distribution, 75 serving as the cut score for presence of disorder, 85 serving as the cut score for prominence of disorder, and 115 corresponding to the maximum raw score. [1]

  7. Diffusion model - Wikipedia

    en.wikipedia.org/wiki/Diffusion_model

    The goal of diffusion models is to learn a diffusion process for a given dataset, such that the process can generate new elements that are distributed similarly as the original dataset. A diffusion model models data as generated by a diffusion process, whereby a new datum performs a random walk with drift through the space of all possible data. [2]