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Self-supervised Learning for DNA sequences with circular dilated convolutional networks

paper: https://doi.org/10.1016/j.neunet.2023.12.002.

Overview of cdilDNA with self-supervised learning.

image

Variant Effect Prediction in Human Tissues.

To pretrain a model you need to follow the steps:

  1. Download GRCH38 from http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz. (3.1G)
  2. Run generate_pretrain_human.py in ./data/. Sequence length [1k, 5k, 10k] and numbers(100k) are required.
  3. Run pretraining.py with the generated data. Configurations of different lengths shall be changed accordingly in config.yaml.

To fine-tune a pretrained model, you need to:

  1. Run generate_data.py in ./data/ to save data. The sequence length is required. A total of 97,922 sequence will be extracted. ve_df.csv needs to be loaded.
  2. Run classify_lightning.py to load the pretrained model under the folder ./human/100k_pretrain_best_epoch_9_8_16.pt and train cdilDNA.

Experimental Results

Model Parameters(k) Running time (ms) Memory (MB) AUROC(%)
CDIL w/ pretrain 39.8 14.51 65.22 88.08
CDIL w/o pretrain 39.8 14.52 65.22 87.50
CNN 39.8 11.60 49.03 85.42
Transformer 33.3 66.65 190.05 66.49
Nyströmformer w/ pretrain 42 35.08 133.19 87.71
Nyströmformer w/o pretrain 42 35.08 133.19 87.50
Performer 41.3 24.25 76.44 87.32
Enformer / / / 84.53

varant_effect result

OCR Prediciton in Plants.

To pretrain a model you need to follow the steps:

  1. Download reference genome files from https://plantdeepsea-toturial2.readthedocs.io/en/latest/08-Statistics.html to ./data/plants/plant_genome/.
  2. Run plant_download.ipynb in ./data/plants/plant_data/ to download plant data.
  3. Run _cython_setup.sh in ./plants/data_cython/.
  4. Run run_pre.py in ./plants/ for required plant.

To fine-tune a pretrained model, you need to:

  1. Run run_epoch.py in ./plants/ with --pre_training. The pretrained models are saved in ./plants/pre/.

Experimental Results

Plant A.thaliana B.distachyon O.sativa-MH O.sativa-ZS S.italica S.bicolor Z.mays
CDIL w/ pretrain 92.90 93.47 93.53 93.09 94.43 96.42 97.21
CDIL w/o pretrain 90.39 91.95 91.45 91.66 92.42 95.80 95.87
CNN 82.02 87.09 86.05 84.94 88.30 91.52 89.79
PlantDeepSEA 90.24 90.59 91.08 90.18 92.39 94.86 95.19
Transformer 76.74 85.63 83.17 83.56 86.27 88.90 84.78
Nyströmformer w/ pretrain 89.80 92.24 90.75 90.50 92.75 95.45 95.20
Nyströmformer w/o pretrain 86.02 90.19 86.92 86.31 90.94 93.67 89.77
Performer 75.95 85.25 82.81 83.02 86.60 88.09 85.13

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  • Python 93.7%
  • Jupyter Notebook 3.4%
  • Cython 2.4%
  • Shell 0.5%