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Related Experiment Video

Updated: Feb 2, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
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chemmodlab: a cheminformatics modeling laboratory R package for fitting and assessing machine learning models.

Jeremy R Ash1, Jacqueline M Hughes-Oliver2

  • 1Department of Statistics, Bioinformatics Research Center, North Carolina State University, 335 Ricks Hall, Campus Box 7566, Raleigh, NC, 27695-7566, USA. jrash@ncsu.edu.

Journal of Cheminformatics
|November 30, 2018
PubMed
Summary

The chemmodlab R package simplifies machine learning model comparison for cheminformatics researchers. It offers streamlined fitting, assessment, and novel visualization of statistically significant performance differences.

Keywords:
Accumulation curveEnrichment factorHit enrichment curveInitial enhancementMachine learningQSARR packageRepeated cross-validation

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Area of Science:

  • Cheminformatics
  • Machine Learning
  • Statistical Modeling

Background:

  • Comparing machine learning models is crucial for research.
  • Existing pipelines can be complex and time-consuming.
  • Standardized assessment methods are needed in cheminformatics.

Purpose of the Study:

  • To introduce chemmodlab, an R package for streamlined machine learning model fitting and assessment.
  • To facilitate easy comparison of model utility for researchers.
  • To provide novel visualization tools for performance differences.

Main Methods:

  • Implementation of expert-accepted model fitting and assessment methods.
  • Development of assessment utilities including accumulation curves and performance measures.
  • Introduction of a multiple comparisons similarity plot for visualizing significant differences.

Main Results:

  • chemmodlab streamlines the model comparison pipeline.
  • The package includes functions for accumulation curves and performance metrics.
  • A novel plot visualizes statistically significant performance differences using repeated k-fold cross-validation.

Conclusions:

  • chemmodlab enhances the ability of researchers to compare machine learning models.
  • The package offers broad utility beyond cheminformatics.
  • It provides efficient and statistically sound methods for model assessment.