ML Anthology

PrePeP: A Light-Weight, Extensible Tool for Predicting Frequent Hitters

Christophe Couronne, Maksim Koptelov, Albrecht Zimmermann
ECML-PKDD 2020 pp. 570-573
doi:10.1007/978-3-030-67670-4_41 /ecmlpkdd/2020/couronne2020ecmlpkdd-prepep/

Abstract

We present PrePeP, a light-weight tool for predicting whether molecules are frequent hitters, and visually inspecting the subgraphs supporting this decision. PrePeP is contains three modules: a mining component, an encoding/predicting component, and a graphical interface, all of which are easily extensible.

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Cite

Text

Couronne et al. "PrePeP: A Light-Weight, Extensible Tool for Predicting Frequent Hitters." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020. doi:10.1007/978-3-030-67670-4_41

Markdown

[Couronne et al. "PrePeP: A Light-Weight, Extensible Tool for Predicting Frequent Hitters." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020.](https://mlanthology.org/ecmlpkdd/2020/couronne2020ecmlpkdd-prepep/) doi:10.1007/978-3-030-67670-4_41

BibTeX

@inproceedings{couronne2020ecmlpkdd-prepep,
  title     = {{PrePeP: A Light-Weight, Extensible Tool for Predicting Frequent Hitters}},
  author    = {Couronne, Christophe and Koptelov, Maksim and Zimmermann, Albrecht},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2020},
  pages     = {570-573},
  doi       = {10.1007/978-3-030-67670-4_41},
  url       = {https://mlanthology.org/ecmlpkdd/2020/couronne2020ecmlpkdd-prepep/}
}