Biological Data Insight & Modeling

Multiomics (genomics, proteomics, lipisomics, etc.) data is typically ill-conditioned with many (coupled) variables and relatively few data records. As a result, such data sets are very difficult to analyze with conventional statistical and machine learning techniques.
ParetoGP assumes we can develop simple algebraic models using just a few of the inputs via an evolutionary search rewarding model simplicity and accuracy. From the thousands of candidate models, we can garner insights on key variables, variable associations and combinations as well as generate concise explainable and human-interpretable models.
In this presentation, we demonstrate the insight and modeling process using DataModeler applied to cancer therapeutics and bone regeneration data sets.

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You are missing paleontology, especially micropaleontology and biostratigraphy