Dataset Information

Inflammatory markers as predictors of depression and anxiety in adolescents: Statistical model building with component-wise gradient boosting.

ABSTRACT: BACKGROUND:Immune system abnormalities have been repeatedly observed in several psychiatric disorders, including severe depression and anxiety. However, whether specific immune mediators play an early role in the etiopathogenesis of these disorders remains unknown. METHODS:In a longitudinal design, component-wise gradient boosting was used to build models of depression, assessed by the Mood-Feelings Questionnaire-Child (MFQC), and anxiety, assessed by the Screen for Child Anxiety Related Emotional Disorders (SCARED) in 254 adolescents from a large set of candidate predictors, including sex, race, 39 inflammatory proteins, and the interactions between those proteins and time. Each model was reduced via backward elimination to maximize parsimony and generalizability. RESULTS:Component-wise gradient boosting and model reduction found that female sex, growth- regulated oncogene (GRO), and transforming growth factor alpha (TGF-alpha) predicted depression, while female sex predicted anxiety. LIMITATIONS:Differential onset of puberty as well as a lack of control for menstrual cycle may also have been responsible for differences between males and females in the present study. In addition, investigation of all possible nonlinear relationships between the predictors and the outcomes was beyond the computational capacity and scope of the present research. CONCLUSIONS:This study highlights the need for novel statistical modeling to identify reliable biological predictors of aberrant psychological behavior.

SUBMITTER: Walss-Bass C

PROVIDER: S-EPMC5895481 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Inflammatory markers as predictors of depression and anxiety in adolescents: Statistical model building with component-wise gradient boosting.

Walss-Bass Consuelo C Suchting Robert R Olvera Rene L RL Williamson Douglas E DE

Journal of affective disorders 20180312

<h4>Background</h4>Immune system abnormalities have been repeatedly observed in several psychiatric disorders, including severe depression and anxiety. However, whether specific immune mediators play an early role in the etiopathogenesis of these disorders remains unknown.<h4>Methods</h4>In a longitudinal design, component-wise gradient boosting was used to build models of depression, assessed by the Mood-Feelings Questionnaire-Child (MFQC), and anxiety, assessed by the Screen for Child Anxiety ...[more]

PMID: 29554616

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Project description:The development of crop varieties with stable performance in future environmental conditions represents a critical challenge in the context of climate change. Environmental data collected at the field level, such as soil and climatic information, can be relevant to improve predictive ability in genomic prediction models by describing more precisely genotype-by-environment interactions, which represent a key component of the phenotypic response for complex crop agronomic traits. Modern predictive modeling approaches can efficiently handle various data types and are able to capture complex nonlinear relationships in large datasets. In particular, machine learning techniques have gained substantial interest in recent years. Here we examined the predictive ability of machine learning-based models for two phenotypic traits in maize using data collected by the Maize Genomes to Fields (G2F) Initiative. The data we analyzed consisted of multi-environment trials (METs) dispersed across the United States and Canada from 2014 to 2017. An assortment of soil- and weather-related variables was derived and used in prediction models alongside genotypic data. Linear random effects models were compared to a linear regularized regression method (elastic net) and to two nonlinear gradient boosting methods based on decision tree algorithms (XGBoost, LightGBM). These models were evaluated under four prediction problems: (1) tested and new genotypes in a new year; (2) only unobserved genotypes in a new year; (3) tested and new genotypes in a new site; (4) only unobserved genotypes in a new site. Accuracy in forecasting grain yield performance of new genotypes in a new year was improved by up to 20% over the baseline model by including environmental predictors with gradient boosting methods. For plant height, an enhancement of predictive ability could neither be observed by using machine learning-based methods nor by using detailed environmental information. An investigation of key environmental factors using gradient boosting frameworks also revealed that temperature at flowering stage, frequency and amount of water received during the vegetative and grain filling stage, and soil organic matter content appeared as important predictors for grain yield in our panel of environments.

Dataset Information

Inflammatory markers as predictors of depression and anxiety in adolescents: Statistical model building with component-wise gradient boosting.

Publications

Inflammatory markers as predictors of depression and anxiety in adolescents: Statistical model building with component-wise gradient boosting.

Similar Datasets

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