- Split code between drg_tools and scripts that use these tools
- Write short tutorials on how to use modules in drg_tools in examples
- Write example .mds on how to use scripts
- Write bpmodel training on bias code
- Implement sequence shifting from left to right for transcripts
- Remove params0.pth and find different way to return to same parameters with random intialization
- Automatically set optimizer to zero after gradient explosion
- Include target mask for data points for every modality
- Include superconvergence learning rate https://arxiv.org/abs/1708.07120
- Use predicted information in a latter layer and pass it back to an earlier layer 3 times.
- This is similar to the alpha fold techniques which seem like a new type of recurrent block, a specialized recurrent block.
- Predict confidence and include unlabeled data points with predicted outputs.
- Try to use automatic gradient descent https://github.com/jxbz/agd
- Create common sequence_embedding_model for cnn_model.py and bpcnn_model.py that is shared between the two networks and identical up to the last layer. Introduce learnable cell type and data modality vector.
- Position invariant convblocks and training for those
- To capture these relative distances between motifs we need long-range equidistant modules
- Allow different conv. blocks for different input sequences
- Work on extended Non-linear convolution model, so that additional layers are not used for motif improvement (i.e. base-pair interactions) but for motif interactions
- Improve long-range convolutions, such as hyena
- Check kwargs handling in model_params.dat file
- Enable DeepLIFT for multiple sequence inputs
- DeepLIFT to multiple random seeds
- Find motifs in attributions with sequence specific cut off from dinuc shuffle
- Write module that can extract sequences from 'sparse'-attribution maps with positions
- Add motif annotations to plot attributions, with motif data base and conv scanning, and with names and location file