Notes

A good point for discussion might be that when not enough samples is available it might be better to use a simpler model than a neural network
What kinds of gene lengths should be used for normalizing? => lets use mean exon lenght
Ensembl ids for rna-seq data are outdated, so retrieving gene lengths is not easy and would require significant amounts of manual annotations
Could add one more task for mutations
- SPL (splicing), EPI (epigenetic), WT (wild type), CTR (control)
Could encode the strand as an additional feature, might be especially useful for feature based gnns where each node is a feature
Fix up the mRMR package packaging
- polars broken
- no checks for whether y is the same length as X
- no checks for duplicate column names -> results in a long computation and eventually a pandas crash
- apparently no cicd to update the pkg on pypi

Name		Name	Last commit message	Last commit date
Latest commit History 65 Commits
logs		logs
mds_data		mds_data
mogonet_disease_best_models		mogonet_disease_best_models
mogonet_risk_best_models		mogonet_risk_best_models
plots		plots
src		src
.gitignore		.gitignore
.python-version		.python-version
README.md		README.md
TODO.md		TODO.md
brca_eval.ipynb		brca_eval.ipynb
brca_preprocessing.ipynb		brca_preprocessing.ipynb
disease_annoations.csv		disease_annoations.csv
ensembl_ids_genes_map.csv		ensembl_ids_genes_map.csv
gene_lengths.gh38.csv		gene_lengths.gh38.csv
interaction_data.tar.gz		interaction_data.tar.gz
mds_data_preprocessing.ipynb		mds_data_preprocessing.ipynb
mds_data_splitting.ipynb		mds_data_splitting.ipynb
mds_disease_eval.ipynb		mds_disease_eval.ipynb
mds_disease_feature_importances.ipynb		mds_disease_feature_importances.ipynb
mds_mrna_top_5000_processed_genes.csv		mds_mrna_top_5000_processed_genes.csv
mds_mutation_eval.ipynb		mds_mutation_eval.ipynb
mds_mutation_feature_importances.ipynb		mds_mutation_feature_importances.ipynb
mds_results_plotting.ipynb		mds_results_plotting.ipynb
mds_risk_eval.ipynb		mds_risk_eval.ipynb
mds_risk_feature_importances.ipynb		mds_risk_feature_importances.ipynb
mutation_annotations.csv		mutation_annotations.csv
pyproject.toml		pyproject.toml
risk_annotations.csv		risk_annotations.csv
rnaseq_samples.csv		rnaseq_samples.csv
uv.lock		uv.lock

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