S3M — Statistically Significant Shapelet Mining

This is the implementation of the method described in the ISMB 2018 paper “Association mapping in biomedical time series via statistically significant shapelet mining”.

Installation

For Mac OS X, we recommend installing S3M using the Homebrew package manager:

$ brew install BorgwardtLab/mlcb/s3m

For Debian and Ubuntu, please use the .deb file of the latest release and install it using:

$ sudo apt install s3m-latest.deb

For Arch Linux, please install S3M from AUR using pacaur or trizen, for example:

$ pacaur -S s3m
$ trizen -S s3m

Please refer to the advanced build instructions for more advanced build processes, including source builds and Docker installations.

Example

We provide a synthetic data set that illustrates the utility of S3M. After installation, the command

$ s3m -i data/example/synthetic.csv -m 15 -o results/example.json

runs S3M on it and stores its results in the results folder. The below figure shows how the method identifies the case-characteristic two spikes as the most significant shapelet (p-value: 5.42e-20).

Help

If you have questions concerning S3M or you encounter problems when trying to build the tool under your own system, please open an issue in the issue tracker. Try to describe the issue in sufficient detail in order to make it possible for us to help you.

Contributors

S3M is developed and maintained by members of the Machine Learning and Computational Biology Lab of Prof. Dr. Karsten Borgwardt:

• Christian Bock (GitHub)
• Thomas Gumbsch (GitHub)
• Max Horn (GitHub)
• Michael Moor (GitHub)
• Bastian Rieck (GitHub)

Citation

Please use the following BibTeX citation when using our method or comparing against it:

@article{Bock18,
  author  = {Bock, Christian and Gumbsch, Thomas and Moor, Michael and Rieck, Bastian and Roqueiro, Damian and Borgwardt, Karsten},
  title   = {Association mapping in biomedical time series via statistically significant shapelet mining},
  journal = {Bioinformatics},
  volume  = {34},
  number  = {13},
  pages   = {i438--i446},
  year    = {2018},
  doi     = {10.1093/bioinformatics/bty246},
}