Regression is a method to estimate parameters in mathematical models of biological systems from experimental data. To ensure the validity of a model for a given data set, pre-regression and post ...

It is imperative for health systems to form data sharing partnerships that allow for the analysis of de-identified clinical data, while effectively and comprehensively protecting patient information.

This data model (i.e. "schema design,") is useful for developing applications around any restricted resource system, not just e-commerce systems.

Missing data is a major issue in many applied problems, especially in the biomedical sciences. We review four common approaches for inference in generalized linear models (GLMs) with missing covariate ...

Traditional scaling approaches to building machine learning models will fail as we run out of data for them. Several techniques may help.

Discover what black box models are, their applications in finance and investing, and examples of how they drive ...

One of the most important steps in desiging a database is establishing the data model. Part one of a two-part article describes how to create a logical model.

Data poisoning is a type of attack that involves tampering with and polluting a machine learning model's training data, impacting the model's ability to produce accurate predictions.