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Cluster analysis (CA), Linear and Quadratic Discriminant Analysis (L(Q DA), Binary Logistic Regression (BLR) and Classification Tree (CT) are applied on two datasets for description of tyrosinase inhibitory activity from molecular structures.
The authors wish to thank JH Price (Vala Sciences, San Diego) for invaluable long-term support in bringing this project to fruition, A Szafran for HTM analysis, L Pasini for help with imaging, and ED Jones for statistical advice.
In our analysis L was equal to 550 bp.
The strict consensus of 125 trees yielded from the MP analysis (L = 82, CI = 0.842, RI = 0.972) was congruent with the majority rule consensus BI tree (Additional file 1: Figure S4).
In the current study, the average noise level was obtained as required for different types of analyses: For the day-by-day analysis, L A e q,24 h was calculated every day since the admission of the patient.
The strict consensus of eight trees from MP analysis (L = 642, CI = 0.872, RI = 0.849) was congruent with the ML tree and the majority rule consensus BI tree (Additional file 1: Figure S3).
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Data sets (5)–(12) are from empirical experiments and measurements: they arise from phylogenetics (5)–(6), image analysis (7), genomics (9), neuroscience (8), medical imaging (10), political science (11), and scientometrics (12).
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