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Supplies mathematical models from pathway representations that use a suite of freely available software. The Path2Models project has generated three types of models: quantitative, kinetic models of metabolic pathways; qualitative, logical models of non-metabolic (primarily signaling) pathways; and genome-scale metabolic reconstructions. This database generates computational models from pathways on a large scale, applying consistent, community-developed and well-supported data formats.


Aims to facilitate the learning of issues in cardiovascular risk scoring. cvdRiskData consists of a synthetic dataset and curricular materials modeled on a real patient cohort that includes clinical and genetic covariates. The script, Bayesian network, and conditional probability tables (CPTs) used to generate the dataset are also included. The dataset can be used for (1) teaching math, statistics, (2) teaching machine learning techniques to biologists and clinicians, and (3) assessing the performance of machine learning techniques on a tunable dataset.