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# 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 lies in its ability to construct a transferable model across gene expression platforms and 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.