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  1. README.html +45 -0
  2. aggregated_matrices/aggregated_attentions/dcm_samples/fine_tuned/max/layer_0/counts.pkl +3 -0
  3. aggregated_matrices/aggregated_attentions/dcm_samples/fine_tuned/max/layer_0/distance_mat.pkl +3 -0
  4. aggregated_matrices/aggregated_attentions/dcm_samples/fine_tuned/max/layer_0/gene_dict.pkl +3 -0
  5. aggregated_matrices/aggregated_attentions/dcm_samples/fine_tuned/max/layer_0/vals.pkl +3 -0
  6. aggregated_matrices/aggregated_attentions/dcm_samples/fine_tuned/max/layer_1/counts.pkl +3 -0
  7. aggregated_matrices/aggregated_attentions/dcm_samples/fine_tuned/max/layer_1/gene_dict.pkl +3 -0
  8. aggregated_matrices/aggregated_attentions/dcm_samples/fine_tuned/max/layer_1/vals.pkl +3 -0
  9. aggregated_matrices/aggregated_attentions/dcm_samples/fine_tuned/max/layer_2/counts.pkl +3 -0
  10. aggregated_matrices/aggregated_attentions/dcm_samples/fine_tuned/max/layer_2/gene_dict.pkl +3 -0
  11. aggregated_matrices/aggregated_attentions/dcm_samples/fine_tuned/max/layer_2/vals.pkl +3 -0
  12. aggregated_matrices/aggregated_attentions/dcm_samples/fine_tuned/max/layer_3/counts.pkl +3 -0
  13. aggregated_matrices/aggregated_attentions/dcm_samples/fine_tuned/max/layer_3/gene_dict.pkl +3 -0
  14. aggregated_matrices/aggregated_attentions/dcm_samples/fine_tuned/max/layer_3/vals.pkl +3 -0
  15. aggregated_matrices/aggregated_attentions/dcm_samples/fine_tuned/max/layer_4/counts.pkl +3 -0
  16. aggregated_matrices/aggregated_attentions/dcm_samples/fine_tuned/max/layer_4/gene_dict.pkl +3 -0
  17. aggregated_matrices/aggregated_attentions/dcm_samples/fine_tuned/max/layer_4/vals.pkl +3 -0
  18. aggregated_matrices/aggregated_attentions/dcm_samples/fine_tuned/max/layer_5/counts.pkl +3 -0
  19. aggregated_matrices/aggregated_attentions/dcm_samples/fine_tuned/max/layer_5/distance_mat.pkl +3 -0
  20. aggregated_matrices/aggregated_attentions/dcm_samples/fine_tuned/max/layer_5/gene_dict.pkl +3 -0
  21. aggregated_matrices/aggregated_attentions/dcm_samples/fine_tuned/max/layer_5/vals.pkl +3 -0
  22. aggregated_matrices/aggregated_attentions/dcm_samples/pretrained/max/counts.pkl +3 -0
  23. aggregated_matrices/aggregated_attentions/dcm_samples/pretrained/max/gene_dict.pkl +3 -0
  24. aggregated_matrices/aggregated_attentions/dcm_samples/pretrained/max/vals.pkl +3 -0
  25. aggregated_matrices/aggregated_attentions/genecorpus/fine_tuned/max/counts.pkl +3 -0
  26. aggregated_matrices/aggregated_attentions/genecorpus/fine_tuned/max/gene_dict.pkl +3 -0
  27. aggregated_matrices/aggregated_attentions/genecorpus/fine_tuned/max/vals.pkl +3 -0
  28. aggregated_matrices/aggregated_attentions/genecorpus/pretrained/max/counts.pkl +3 -0
  29. aggregated_matrices/aggregated_attentions/genecorpus/pretrained/max/gene_dict.pkl +3 -0
  30. aggregated_matrices/aggregated_attentions/genecorpus/pretrained/max/vals.pkl +3 -0
  31. aggregated_matrices/aggregated_embeddings/dcm_samples/fine_tuned/max/layer_0/counts.pkl +3 -0
  32. aggregated_matrices/aggregated_embeddings/dcm_samples/fine_tuned/max/layer_0/gene_dict.pkl +3 -0
  33. aggregated_matrices/aggregated_embeddings/dcm_samples/fine_tuned/max/layer_0/vals.pkl +3 -0
  34. aggregated_matrices/aggregated_embeddings/dcm_samples/fine_tuned/max/layer_1/counts.pkl +3 -0
  35. aggregated_matrices/aggregated_embeddings/dcm_samples/fine_tuned/max/layer_1/gene_dict.pkl +3 -0
  36. aggregated_matrices/aggregated_embeddings/dcm_samples/fine_tuned/max/layer_1/vals.pkl +3 -0
  37. aggregated_matrices/aggregated_embeddings/dcm_samples/fine_tuned/max/layer_2/counts.pkl +3 -0
  38. aggregated_matrices/aggregated_embeddings/dcm_samples/fine_tuned/max/layer_2/gene_dict.pkl +3 -0
  39. aggregated_matrices/aggregated_embeddings/dcm_samples/fine_tuned/max/layer_2/vals.pkl +3 -0
  40. aggregated_matrices/aggregated_embeddings/dcm_samples/fine_tuned/max/layer_3/counts.pkl +3 -0
  41. aggregated_matrices/aggregated_embeddings/dcm_samples/fine_tuned/max/layer_3/gene_dict.pkl +3 -0
  42. aggregated_matrices/aggregated_embeddings/dcm_samples/fine_tuned/max/layer_3/vals.pkl +3 -0
  43. aggregated_matrices/aggregated_embeddings/dcm_samples/fine_tuned/max/layer_4/counts.pkl +3 -0
  44. aggregated_matrices/aggregated_embeddings/dcm_samples/fine_tuned/max/layer_4/gene_dict.pkl +3 -0
  45. aggregated_matrices/aggregated_embeddings/dcm_samples/fine_tuned/max/layer_4/vals.pkl +3 -0
  46. aggregated_matrices/aggregated_embeddings/dcm_samples/fine_tuned/max/layer_5/counts.pkl +3 -0
  47. aggregated_matrices/aggregated_embeddings/dcm_samples/fine_tuned/max/layer_5/gene_dict.pkl +3 -0
  48. aggregated_matrices/aggregated_embeddings/dcm_samples/fine_tuned/max/layer_5/vals.pkl +3 -0
  49. aggregated_matrices/aggregated_embeddings/dcm_samples/fine_tuned/max/layer_input/counts.pkl +3 -0
  50. aggregated_matrices/aggregated_embeddings/dcm_samples/fine_tuned/max/layer_input/gene_dict.pkl +3 -0
README.html ADDED
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+ <html><head><style>body {
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+ color: black;
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+ }
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+ </style></head><body><h1 id="aggregated-weight-matrices-used-in-transformers-enhance-the-predictive-power-of-network-medicine-">Aggregated Weight Matrices Used in <em>Transformers Enhance the Predictive Power of Network Medicine</em></h1>
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+ <p><em>Jonah Spector, Andrés Aldana Gonzalez, Michael Sebek, Joseph Ehlert, Christian DeFrondeville, Albert-László Barabási</em></p>
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+ <h2 id="data-structure">Data Structure</h2>
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+ <p>This directory contains all aggregated weight matrices used to make figures in the manuscript. The data structure is as follows</p>
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+ <pre><code>aggregated_matrices
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+ aggregated_attentions
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+ dcm_samples
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+ fine_tuned
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+ <span class="hljs-built_in">max</span>
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+ layer_0
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+ layer_1
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+ layer_2
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+ layer_3
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+ layer_4
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+ layer_5
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+ pretrained
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+ <span class="hljs-built_in">max</span>
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+ genecorpus
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+ fine_tuned
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+ <span class="hljs-built_in">max</span>
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+ pretrained
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+ <span class="hljs-built_in">max</span>
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+ aggregated_embeddings
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+ dcm_samples
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+ fine_tuned
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+ <span class="hljs-built_in">max</span>
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+ layer_input
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+ layer_0
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+ layer_1
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+ layer_2
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+ layer_3
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+ layer_4
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+ layer_5
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+ pretrained
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+ <span class="hljs-built_in">max</span>
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+ genecorpus
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+ fine_tuned
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+ <span class="hljs-built_in">max</span>
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+ pretrained
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+ <span class="hljs-built_in">max</span>
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+ </code></pre><p>In each <code>max</code> folder, there are 3 files, called <code>counts.pkl</code>, <code>vals.pkl</code>, <code>gene_dict.pkl</code>. The counts matrix is a square matrix of size NxN, where N is the number of unique genes in the sample set. It counts how many times each pair of genes co-occurs in a sample. The vals matrix is the same shape, and contains the aggregated weight between each pair of genes. <code>gene_dict.pkl</code> is a dictionary that specifies the index of each gene in the matrix.</p>
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+ </body></html>
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