ArticleMolecular informatics2018
Classifiers and their Metrics Quantified.
Article in Molecular informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
21 citing papers in PubMed.
- New Machine Learning Models for Predicting the Organic Cation Transporters OCT1, OCT2, and OCT3 Uptake.ACS omega · 2025Article
- Off-the-Shelf Large Language Models for Causality Assessment of Individual Case Safety Reports: A Proof-of-Concept with COVID-19 Vaccines.Drug safety · 2025Article
- Deployment of amBio · 2025Article
- Reliability and Validity of Self-Reported Risk Factors for Stroke and Dementia.Journal of the American Heart Association · 2025Article
- Evaluating the three-level approach of the U-smile method for imbalanced binary classification.PloS one · 2025Article
- A Machine Learning-Based Web Tool for the Severity Prediction of COVID-19.Biotech (Basel (Switzerland)) · 2024Article
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- Deployment of abioRxiv : the preprint server for biology · 2024Article
- Comparison of machine learning techniques in prediction of mortality following cardiac surgery: analysis of over 220 000 patients from a large national database.European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery · 2023Article
- A User's Guide to Machine Learning for Polymeric Biomaterials.ACS polymers Au · 2023Review
- PToPI: A Comprehensive Review, Analysis, and Knowledge Representation of Binary Classification Performance Measures/Metrics.SN computer science · 2023Article
- G4Boost: a machine learning-based tool for quadruplex identification and stability prediction.BMC bioinformatics · 2022Article
- HybridACS pharmacology & translational science · 2021Article
- Article
- Development of Machine Learning Models for Accurately Predicting and Ranking the Activity of Lead Molecules to Inhibit PRC2 Dependent Cancer.Pharmaceuticals (Basel, Switzerland) · 2021Article
- Application 2D Descriptors and Artificial Neural Networks for Beta-Glucosidase Inhibitors Screening.Molecules (Basel, Switzerland) · 2020Article
- Recommender Systems in Antiviral Drug Discovery.ACS omega · 2020Article
- Artificial Intelligence-Based Differential Diagnosis: Development and Validation of a Probabilistic Model to Address Lack of Large-Scale Clinical Datasets.Journal of medical Internet research · 2020Article
- The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation.BMC genomics · 2020Article
- Applicability Domain of Active Learning in Chemical Probe Identification: Convergence in Learning from Non-Specific Compounds and Decision Rule Clarification.Molecules (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular modeling frequently constructs classification models for the prediction of two-class entities, such as compound bio(in)activity, chemical property (non)existence, protein (non)interaction, and so forth. The models are evaluated using well known metrics such as accuracy or true positive rates. However, these frequently used metrics applied to retrospective and/or artificially generated prediction datasets can potentially overestimate true performance in actual prospective experiments. Here, we systematically consider metric value surface generation as a consequence of data balance, and propose the computation of an inverse cumulative distribution function taken over a metric surface. The proposed distribution analysis can aid in the selection of metrics when formulating study design. In addition to theoretical analyses, a practical example in chemogenomic virtual screening highlights the care required in metric selection and interpretation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.