Model-based molecular phylogenetics plays an important role in comparisons of genomic data, and model selection is a key step in all such analyses. We present ModelFinder, a fast model-selection ...

Model fit can be assessed using the difference between the model's predictions and new data (prediction error—our focus this month) or between the estimated and ...

The purpose of statistical model selection is to identify a parsimonious model, which is a model that is as simple as possible while maintaining good predictive ability over the outcome of interest.

This important study introduces a new biology-informed strategy for deep learning models aiming to predict mutational effects in antibody sequences. It provides solid evidence that separating ...

Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision-making. In both traditional ...

When AI models fail to meet expectations, the first instinct may be to blame the algorithm. But the real culprit is often the data—specifically, how it’s labeled. Better data annotation—more accurate, ...

How to use Excel’s Data Model to turn related data into meaningful information Your email has been sent Excel's Data Model feature allows you to build relationships between data sets for easier ...