Application of the deep learning algorithm in nutrition research – using serum pyridoxal 5′-phosphate as an example was written by Ma, Chaoran;Chen, Qipin;Mitchell, Diane C.;Na, Muzi;Tucker, Katherine L.;Gao, Xiang. And the article was included in Nutrition Journal in 2022.Category: pyridine-derivatives The following contents are mentioned in the article:
Abstract: Background: Multivariable linear regression (MLR) models were previously used to predict serum pyridoxal 5′-phosphate (PLP) concentration, the active coenzyme form of vitamin B6, but with low predictability. We developed a deep learning algorithm (DLA) to predict serum PLP based on dietary intake, dietary supplements, and other potential predictors. Methods: This cross-sectional anal. included 3778 participants aged ≥20 years in the National Health and Nutrition Examination Survey (NHANES) 2007-2010, with completed information on studied variables. Dietary intake and supplement use were assessed with two 24-h dietary recalls. We included potential predictors for serum PLP concentration in the models, including dietary intake and supplement use, sociodemog. variables (age, sex, race-ethnicity, income, and education), lifestyle variables (smoking status and phys. activity level), body mass index, medication use, blood pressure, blood lipids, glucose, and C-reactive protein. We used a 4-hidden-layer deep neural network to predict PLP concentration, with 3401 (90%) participants for training and 377 (10%) participants for test using random sampling. We obtained outputs after sending the features of the training set and conducting forward propagation. We then constructed a loss function based on the distances between outputs and labels and optimized it to find good parameters to fit the training set. We also developed a prediction model using MLR. Results: After training for 105 steps with the Adam optimization method, the highest R2 was 0.47 for the DLA and 0.18 for the MLR model in the test dataset. Similar results were observed in the sensitivity analyses after we excluded supplement-users or included only variables identified by stepwise regression models. Conclusions: DLA achieved superior performance in predicting serum PLP concentration, relative to the traditional MLR model, using a nationally representative sample. As preliminary data analyses, the current study shed light on the use of DLA to understand a modifiable lifestyle factor. This study involved multiple reactions and reactants, such as (4-Formyl-5-hydroxy-6-methylpyridin-3-yl)methyl dihydrogen phosphate (cas: 54-47-7Category: pyridine-derivatives).
(4-Formyl-5-hydroxy-6-methylpyridin-3-yl)methyl dihydrogen phosphate (cas: 54-47-7) belongs to pyridine derivatives. Pyridine’s the lone pair does not contribute to the aromatic system but importantly influences the chemical properties of pyridine, as it easily supports bond formation via an electrophilic attack. Pyridine derivatives are also useful as small-molecule α-helix mimetics that inhibit protein-protein interactions, as well as functionally selective GABA ligands.Category: pyridine-derivatives