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CommonNNClustering─A Python Package for Generic Common-Nearest-Neighbor Clustering.

Jan-Oliver Kapp-JoswigBettina G Keller
Published in: Journal of chemical information and modeling (2023)
Density-based clustering procedures are widely used in a variety of data science applications. Their advantage lies in the capability to find arbitrarily shaped and sized clusters and robustness against outliers. In particular, they proved effective in the analysis of molecular dynamics simulations, where they serve to identify relevant, low-energetic molecular conformations. As such, they can provide a convenient basis for the construction of kinetic (core-set) Markov-state models. Here we present the open-source Python project CommonNNClustering, which provides an easy-to-use and efficient reimplementation of the common-nearest-neighbor (CommonNN) method. The package provides functionalities for hierarchical clustering and an evaluation of the results. We put our emphasis on a generic API design to keep the implementation flexible and open for customization.
Keyphrases
  • molecular dynamics simulations
  • single cell
  • rna seq
  • quality improvement
  • molecular docking
  • primary care
  • public health
  • minimally invasive
  • healthcare
  • big data
  • single molecule
  • data analysis
  • artificial intelligence