(a, b). The statistical error analyses for the models using the same dataset. by Fahd Saeed Alakbari (10701871)

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FT-IR results. by Caglar Akcay (6910697)

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Analyzing the relationship between UACR and cognitive scores and impairment. by Yubo Teng (21246745)

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The comparison of ν_s – RHOB trends models with experimental measurements. by Fahd Saeed Alakbari (10701871)

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Complete results of regression analyses of the candidate gene approach. by Dai Nogimura (21445649)

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The comparison of ν_s – DTs trends models with experimental measured values for RHOB = 2.33 g/cc and DTc = 118.67 us/ft. by Fahd Saeed Alakbari (10701871)

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Distribution of continuous variables of all 3 visits (visit 1 = day 0, visit 2 = day 35, and visit 3 = day 70) of the PCSO-524 group and the placebo group. Results are shown as mea... by Nattapon Riengvirodkij (11268720)

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eQTL analysis for proxy SNPs of <i>PRMT6</i>-<i>NTNG1</i> rs12028323. by Dai Nogimura (21445649)

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The optimized GPR approach’s cross-plotting for the training dataset. by Fahd Saeed Alakbari (10701871)

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Methodology flowchart. by Fahd Saeed Alakbari (10701871)

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The most common machine learning methods to predict ν_s. by Fahd Saeed Alakbari (10701871)

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Tricuspid regurgitation pressure gradient of all 17 dogs. by Nattapon Riengvirodkij (11268720)

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Trend analysis of the ν_s – DTc in the range found in the dataset used for the optimized GPR. by Fahd Saeed Alakbari (10701871)

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Statistical error analyses of the proposed or optimized GPR model. by Fahd Saeed Alakbari (10701871)

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FT-IR results. by Caglar Akcay (6910697)

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Density differences between pre- and post-measurements for program completers (N = 160). by Anton Kurapov (11346019)

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Fracture pressure based on proposed GPR Poisson’s ratio model. by Fahd Saeed Alakbari (10701871)

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Tricuspid regurgitation pressure gradient in the PCSO-524 and placebo groups. by Nattapon Riengvirodkij (11268720)

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Baseline characteristics of participants (N = 2385) by UACR. by Yubo Teng (21246745)

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Comparison of ν_s measured and predicted estimates of the optimized GPR approach for (a) training, (b) validation, and (c) testing datasets. by Fahd Saeed Alakbari (10701871)

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