TOP
TOP constructs a transferable model across gene expression platforms for prospective experiments. Such a transferable model can be trained to make predictions on independent validation data with an accuracy that is similar to a re-substituted model. The TOP procedure also has the flexibility to be adapted to suit the most common clinical response variables, including linear response, binomial and Cox PH models.
README
TOP Due to data scale differences between multiple omics data, a model constructed from a training data tends to have poor prediction power on a validation data. While the usual bioinformatics approach is to re-normalise both the training and the validation data, this step may not be possible due to ethics constrains. TOP avoids re-normalisation of additional data through the use of log-ratio features and thus also enable prediction for single omics samples. The novelty of the TOP procedure…
- Repository
- github.com/harry25r/top
Source attribution
- GitHub — github.com/harry25r/top
- Bioconductor — TOP
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