ArticleMolecules (Basel, Switzerland)2019
Multi-Level Comparison of Machine Learning Classifiers and Their Performance Metrics.
Article in Molecules (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
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- From Latent Manifolds to Targeted Molecular Probes: An Interpretable, Kinome-Scale Generative Machine Learning Framework for Family-Based Kinase Ligand Design.Biomolecules · 2026Article
- Evaluation of single-template ligand-based methods for the discovery of small-molecule nucleic acid binders.Briefings in bioinformatics · 2025Article
- Assessment of university students' earthquake coping strategies using artificial intelligence methods.Scientific reports · 2025Article
- Direct profiling of non-adenosines in poly(A) tails of endogenous and therapeutic mRNAs with Ninetails.Nature communications · 2025Article
- CSEL-BGC: A Bioinformatics Framework Integrating Machine Learning for Defining the Biosynthetic Evolutionary Landscape of Uncharacterized Antibacterial Natural Products.Interdisciplinary sciences, computational life sciences · 2025Article
- Machine learning assisted classification RASAR modeling for the nephrotoxicity potential of a curated set of orally active drugs.Scientific reports · 2025Article
- Comparing machine learning models with a focus on tone in grooming chat logs.Frontiers in pediatrics · 2025Article
- Aerial image segmentation of embankment dams based on multispectral remote sensing: a case study in the Belo Monte Hydroelectric Complex, Pará, Brazil.PeerJ. Computer science · 2025Article
- Machine learning algorithms able to predict the prognosis of gastric cancer patients treated with immune checkpoint inhibitors.World journal of gastroenterology · 2024Article
- The application of chemical similarity measures in an unconventional modeling framework c-RASAR along with dimensionality reduction techniques to a representative hepatotoxicity dataset.Scientific reports · 2024Article
- In Silico Exploration of Novel EGFR Kinase Mutant-Selective Inhibitors Using a Hybrid Computational Approach.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Harnessing Machine Learning to Uncover Hidden Patterns in Azole-Resistant CYP51/ERG11 Proteins.Microorganisms · 2024Article
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- Advanced machine learning approach for DoS attack resilience in internet of vehicles security.Heliyon · 2024Article
- Construction and validation of machine learning algorithm for predicting depression among home-quarantined individuals during the large-scale COVID-19 outbreak: based on Adaboost model.BMC psychology · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Machine learning classification algorithms are widely used for the prediction and classification of the different properties of molecules such as toxicity or biological activity. the prediction of toxic vs. non-toxic molecules is important due to testing on living animals, which has ethical and cost drawbacks as well. The quality of classification models can be determined with several performance parameters. which often give conflicting results. In this study, we performed a multi-level comparison with the use of different performance metrics and machine learning classification methods. Well-established and standardized protocols for the machine learning tasks were used in each case. The comparison was applied to three datasets (acute and aquatic toxicities) and the robust, yet sensitive, sum of ranking differences (SRD) and analysis of variance (ANOVA) were applied for evaluation. The effect of dataset composition (balanced vs. imbalanced) and 2-class vs. multiclass classification scenarios was also studied. Most of the performance metrics are sensitive to dataset composition, especially in 2-class classification problems. The optimal machine learning algorithm also depends significantly on the composition of the dataset.
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