Figure 2
From: Novel applications of Convolutional Neural Networks in the age of Transformers
DeepMapper architecture. DeepMapper uses sequence or multi-variate data as input. The first step of DeepMapper is to merge and if required index input files to prepare them into matrix format. The data are normalised using log normalisation, then folded to a matrix. Folding is performed either directly with the natural order of the data or by using the index that is generated or supplied during the data import. After folding, the data are kept in temporary storage and separated to ‘train’ and ‘test’ using SciPy train test split. Training is done using either using CNNs that are supplied by the PyTorch libraries, or a custom CNN supplied (ResNet18 is used by default). Intermediary results are run through attribution algorithms supplied by the Captum51 and saved to run history log. The run is then repeated until convergence is achieved, or until a pre-determined number of iterations are performed by shuffling training testing and validation data. Results are summarised in a report with exportable tables and graphics. Attribution is applied to true positives and true negatives, and these are translated back to features to be added to reports. Further details can be directly found in the accompanying code50.
