Overall, if we rely on the signal from familial structure to drive prediction, then a dense, whole-genome linear predictor, such as RR or G-BLUP with all the available SNPs as input, is a good model choice, especially for traits that have many small genetic effects and high heritability, such as height.
At DataRobot we have built in capabilities (Feature Impact, Prediction Explanations, Partial Dependence) that explain what data drives predictions, when that data matters and when it doesn't, and explains individual predictions.
Here, we demonstrate an iterative metabolic engineering effort that integrates computationally driven predictions and metabolic flux analysis techniques to meet this challenge.
We recently demonstrated the integrated approach of computationally driven predictions and metabolic flux analysis techniques for the overproduction of FAs with different chain lengths (Ranganathan et al., 2012).
The major advantage of the pan-specific approach is that it predicts the binding of any peptide to any present and future HLA molecule, even in absence of data specific for the query HLA molecule, whereas conventional data-driven prediction approaches are restricted to predict the binding of peptides solely to the particular HLA molecules included in the training.
Code for data processing is available at https://github.com/rdbraatz/data-driven-prediction-of-battery-cycle-life-before-capacity-degradation.
Tatonetti, N. P., Ye, P. P., Daneshjou, R. & Altman, R. B. Data-driven prediction of drug effects and interactions.
These results are available online as part of HumanBase, a data-driven prediction system about human biology and disease developed by the research team.
The pilot project will rely on software and services provided by Consensus Point, a Nashville-based company that has helped to build employee-driven prediction markets for several major companies, including General Electric, Best Buy, and Qualcomm.
