Systems and methods for predicting likely phenotypic outcomes using mathematical models and given genetic, phenotypic and/or clinical data of an individual, and also relevant aggregated medical data consisting of genotypic, phenotypic, and/or clinical data from germane patient subpopulations are provided. In one embodiment, support vector machines may be used to create non-linear models, or LASSO techniques may be used to create linear models, both of which are trained using convex optimization techniques to make the models sparse. In another embodiment, phenotypic predictions may be made using models based on contingency tables for genetic data that can be constructed from data available in genomic databases.


> Computational model of the internal human pelvic environment

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