Default and optimized parameters for each predictive model. by Emily S. Nichols (8740029)

Hierarchical clustering to infer a binary tree with <i>K</i> = 4 sampled populations. by Tristan Mary-Huard (3864)

Personal algorithmic recommendations of potassium and phosphorous intake and the corresponding concentration predictions. by Jari Turkia (17912475)

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Top-performing model configuration and performance by algorithm type. by Uğur Ejder (22683228)

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DataSheet1_Evolutionary algorithm for the optimization of meal intake and insulin administration in patients with type 2 diabetes.PDF by Eva Gonzalez-Flo (5046737)

Data collection during the patients’ usual treatment routine. by Jari Turkia (17912475)

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The statistical description of the original data set of the patients (<i>n</i> = 162). by Uğur Ejder (22683228)

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The list of parameters of the modified data set for machine learning (<i>n</i> = 162). by Uğur Ejder (22683228)

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Identification and quantitation of clinically relevant microbes in patient samples: Comparison of three k-mer based classifiers for speed, accuracy, and sensitivity by George S. Watts (7962206)

“…We tested the accuracy, sensitivity, and resource requirements of three top metagenomic taxonomic classifiers that use fast k-mer based algorithms: Centrifuge, CLARK, and KrakenUniq. Binary mixtures of bacteria showed all three reliably identified organisms down to 1% relative abundance, while only the relative abundance estimates of Centrifuge and CLARK were accurate. …”

Conceptual model for pathways and genes determining cholesterol plasma levels used van de Pas and colleagues [9], [10]. by Francesco Reggiani (5727733)

Personal recommendations of potassium and phosphorous intake () based on cross-validation predictions of nutrition effects, . Other parameters of the figure are similar to in-sampl... by Jari Turkia (17912475)

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Personalized normal ranges for plasma concentrations depending on the age of the patient. by Jari Turkia (17912475)

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Intake levels of potassium and phosphorous from the collected food records and their observed effects on these concentrations. by Jari Turkia (17912475)

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Correlation plot of matrix C_<i>g</i> that includes the estimated within-model and cross-model correlations in level of dialysis treatment. by Jari Turkia (17912475)

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Posterior predictive check (PPC) of the final nutritional effect model. by Jari Turkia (17912475)

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Structure of the graphical model with general, treatment, and personal level nutrient effects. by Jari Turkia (17912475)

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Comparison of model structures for personal nutrient effects. by Jari Turkia (17912475)

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Strongest and personally most varying effects of nutrients and medication. by Jari Turkia (17912475)

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Figure shows normalized root mean square error (NRMSE) of the two-level model for each patient. by Jari Turkia (17912475)

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Personal details that are used as predictors. by Jari Turkia (17912475)

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