Code for analyses in "Obesity and risk of female reproductive disorders: A Mendelian Randomisation Study"

Code to reproduce analysis and figures for 'Genetic mapping of etiologic brain cell types for obesity' (Timshel, eLife 2020)

🍎 A Reproducible Pipeline for Processing SISVAN Microdata on Nutritional Status Monitoring in Brazil (2008-2023)

ObMetrics is a Shiny app developed to facilitate the calculation of outcomes related to Metabolic Syndrome in pediatric populations. This repository contains documentation and licensing details for the application, which aims to provide a user-friendly interface for healthcare professionals and researchers.

This notebook presents a concise analysis for predicting obesity risk using machine learning models like Random Forest and XGBoost. Focused on identifying key factors influencing obesity through exploratory data analysis (EDA) and predictive modeling, the notebook offers insights into effective prevention strategies.

OCS (BP): Examine global patterns of obesity across rural and urban regions

Estimation of Obesity Levels

Scripts for assessing longitudinal quantitative traits in UKBIOBANK-linked primary care data

Python & R scripts collection for AdipoAtlas project

Codes for the statistical analysis that investigates the impact of high-fat diet on gut microbiome and serotonergic gene expression in the raphe nuclei.

Repository to preview, describe, and link to multiple health-related Tableau dashboards.

Using D3, this repository takes the data from the US Census Bureau's 2014 ACS 1-year estimates and creates animated visualizations from it.

Android app that predicts chronic disease risk such as diabetes, cancer, obesity, cardiovascular diseases based on user health data, written in kotlin and jetpack compose.

Classification of Obesity Status in Indonesia Using XGBoost & ADASYN-N Method

CHAMP data wrangling codes: cleaning, reshaping and quantitive analysis of child measurement data

Data analysis by Yuchang Bao, Yulin Huang, He Yang

[In Production] Adaptation of Nathaniel Daw's Two-Step Sequential Learning Task. Designed for a study of reward prediction for food with college undergraduates.

Use of OLS method, Linear Regression, K-means, Agglomerative Hierarchical, DBSCAN, Decision Tree, Random Forest, Logistic Regression, Support Vector Classifier, K-nearest neighbors, and Naive Bayes algorithms in the case study to estimate obesity levels.