Predicting successful CME events using only clathrin markers. 🦠📈

Xiao Li 14c0bcaf1b segmentation with max overall cd scores 19 hours ago
data 97fad9a70f add test for pipeline 2 months ago
docs 84a8b038cb add cd scores 1 month ago
lib c41f8b5f73 add docs 4 months ago
models 14c0bcaf1b segmentation with max overall cd scores 19 hours ago
notebooks 14c0bcaf1b segmentation with max overall cd scores 19 hours ago
reports 6e017c7b5b reg w/ full data 1 week ago
src 3cefe2291c add dnn video net pretrained 1 month ago
tests 97fad9a70f add test for pipeline 2 months ago
.gitignore 97fad9a70f add test for pipeline 2 months ago
_config.yml 46bb1724b6 Set theme jekyll-theme-cayman 5 months ago
readme.md 8639e86ef8 move fig nbs to reports folder 1 month ago
requirements.txt 84a8b038cb add cd scores 1 month ago

readme.md

predicting auxilin spikes in clathrin-mediated endocytosis

quickstart for making new predictions

  • our fully trained model is available in the models folder

reproducibility

  • download data: download cached data after tracking from this gdrive folder - should be added to the folder data/tracks
  • process data: run python data.py to properly preprocess all the data (will cache it into the "processed folder")
  • rerun analysis: notebooks folder contains step-by-step analysis
  • tests: run tests with pytest in the tests folder

acknowledgements