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Data Science Resources
Tools & Models
Ki is using state-of-the-art tools, including modeling and visualization methods, to understand and analyze data in the Ki knowledge base.
By curating and aggregating data sets into the Ki knowledge base, we can work with collaborators to use these tools to ask bigger and broader questions about healthy birth, growth, and neurocognitive development.
A Ki model catalog is being created to organize Ki models that are at various stages of development and testing. Here are some of the tools and models that we have developed to explore the growing knowledge base. Check back in late 2018 for updates.
Ki tools are interactive applications that are designed to explore data and advance learning to promote healthy birth, growth, and development. The information explored with Ki tools includes existing knowledge (Seminal Events Timeline), isolated data sets (Trelliscope), and integrated data sets (Full Random Effects Model Explorer; Study Explorer).
Ki uses a range of methodologies because analyzing different kinds of data in different ways reveals more insights. Some of these methodologies (e.g., functional principal component analysis and machine learning) are based entirely on observed patterns within data. Others (e.g., mathematical models of biological systems) are driven by statistical assumptions about the data based on researchers’ biological understanding of the process being modeled. Many methods lie somewhere in the middle of this spectrum of assumptions.
Empirical models help us understand study data and identify key trends by fitting model curves to the measured data. HBGDki empirical models include the Full Random Effects Model (FREM) that describes growth patterns in height- (HAZ) and weight-for-age z-score (WAZ), and the Development score (D-score) to model observations about cognitive development.
To develop a data-driven method for combining multivariate outcome measures (e.g., achievement scores) into a composite score in situations where investigators lack scientific grounding for the use of other composite scores.VIEW MODEL SUMMARY >
Mechanistic models describe underlying biological mechanisms that are relevant to growth and development outcomes. HBGDki mechanistic models use data from published studies to quantitatively characterize the interactions of nutrients (quantity and quality), gut function, maternal-fetal interactions, infectious and noninfectious microbes, and environmental enteropathy pathways that affect birth, growth, and neurodevelopmental outcomes.
To construct a physiologically-based mathematical representation of energy intake, expendature, and growth, incorporating influences of microbiota health, gastrointestinal pathogen infection, mucosal immune activation and inflammation.VIEW MODEL SUMMARY >
To construct a semi-mechanistic model which describes fetal growth (weight, length and body composition) based on maternal protein-energy availability and maternal body and tissue (protein and lipid) composition.VIEW MODEL SUMMARY >
A biologically informed mathematical model simulating growth of an infant from birth to 5 years of age including brain, muscle, adipose and other lean compartments.VIEW MODEL SUMMARY >
0-5 years old
Causal models describe cause and effect, and may establish how an intervention or combination of interventions may affect physical growth or neurocognitive development. HBGDki causal models are created with methods such as network meta-analysis to determine the relative efficacy between interventions that have not been compared directly in a clinical trial, and computer reading of large volumes of published research to learn about connections between different causal relations.
To identify causal pathways linking enteric pathogen transmission and children's dietary intake with impaired growth to inform which intervention pathways may be most effective.VIEW MODEL SUMMARY >
Population models help us understand how the burden of disease varies between different populations or over time. HBGDki population models evaluate heterogeneity between different populations to determine the most important risk factors to a population and potential interventions that may be most effective in a population, and categorize countries on the basis of risk factors for disease instead of geography or environment.