ArticlePloS one2015
Computed ABC Analysis for Rational Selection of Most Informative Variables in Multivariate Data.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- A model-agnostic framework for dataset-specific selection of missing value imputation methods in pain-related numerical data.Canadian journal of pain = Revue canadienne de la douleur · 2026Article
- Revisiting Fold-Change Calculation: Preference for Median or Geometric Mean over Arithmetic Mean-Based Methods.Biomedicines · 2024Article
- Development of an explainable AI system using routine clinical parameters for rapid differentiation of inflammatory conditions.Frontiers in immunology · 2024Article
- Machine learning identifies right index finger tenderness as key signal of DAS28-CRP based psoriatic arthritis activity.Scientific reports · 2023Article
- Machine learning analysis predicts a person's sex based on mechanical but not thermal pain thresholds.Scientific reports · 2023Article
- Recursive computed ABC (cABC) analysis as a precise method for reducing machine learning based feature sets to their minimum informative size.Scientific reports · 2023Article
- Prediction of Clinical Outcomes with Explainable Artificial Intelligence in Patients with Chronic Lymphocytic Leukemia.Current oncology (Toronto, Ont.) · 2023Article
- Following Evidence-Based Recommendations for Perioperative Pain Management after Cesarean Section Is Associated with Better Pain-Related Outcomes: Analysis of Registry Data.Journal of clinical medicine · 2023Article
- Robust Classification Using Posterior Probability Threshold Computation Followed by Voronoi Cell Based Class Assignment Circumventing Pitfalls of Bayesian Analysis of Biomedical Data.International journal of molecular sciences · 2022Article
- A Bioinformatics View on Acute Myeloid Leukemia Surface Molecules by Combined Bayesian and ABC Analysis.Bioengineering (Basel, Switzerland) · 2022Article
- Sorting of Odor Dilutions Is a Meaningful Addition to Assessments of Olfactory Function as Suggested by Machine-Learning-Based Analyses.Journal of clinical medicine · 2022Article
- Euclidean distance-optimized data transformation for cluster analysis in biomedical data (EDOtrans).BMC bioinformatics · 2022Article
- Machine-Learning Analysis of Serum Proteomics in Neuropathic Pain after Nerve Injury in Breast Cancer Surgery Points at Chemokine Signaling via SIRT2 Regulation.International journal of molecular sciences · 2022Article
- Machine Learning Refutes Loss of Smell as a Risk Indicator of Diabetes Mellitus.Journal of clinical medicine · 2021Article
- Machine-Learning Points at Endoscopic, Quality of Life, and Olfactory Parameters as Outcome Criteria for Endoscopic Paranasal Sinus Surgery in Chronic Rhinosinusitis.Journal of clinical medicine · 2021Article
- Drugs and Epigenetic Molecular Functions. A Pharmacological Data Scientometric Analysis.International journal of molecular sciences · 2021Article
- Data-science based analysis of perceptual spaces of odors in olfactory loss.Scientific reports · 2021Article
- Computational Functional Genomics-Based AmpliSeq™ Panel for Next-Generation Sequencing of Key Genes of Pain.International journal of molecular sciences · 2021Article
- Machine-Learned Association of Next-Generation Sequencing-Derived Variants in Thermosensitive Ion Channels Genes with Human Thermal Pain Sensitivity Phenotypes.International journal of molecular sciences · 2020Article
- Machine-learning-derived rules set excludes risk of Parkinson's disease in patients with olfactory or gustatory symptoms with high accuracy.Journal of neurology · 2020Article
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Authors and funding
2 authors.
Funding
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Abstract
objectiveMultivariate data sets often differ in several factors or derived statistical parameters, which have to be selected for a valid interpretation. Basing this selection on traditional statistical limits leads occasionally to the perception of losing information from a data set. This paper proposes a novel method for calculating precise limits for the selection of parameter sets.
methodsThe algorithm is based on an ABC analysis and calculates these limits on the basis of the mathematical properties of the distribution of the analyzed items. The limits implement the aim of any ABC analysis, i.e., comparing the increase in yield to the required additional effort. In particular, the limit for set A, the "important few", is optimized in a way that both, the effort and the yield for the other sets (B and C), are minimized and the additional gain is optimized.
resultsAs a typical example from biomedical research, the feasibility of the ABC analysis as an objective replacement for classical subjective limits to select highly relevant variance components of pain thresholds is presented. The proposed method improved the biological interpretation of the results and increased the fraction of valid information that was obtained from the experimental data.
conclusionsThe method is applicable to many further biomedical problems including the creation of diagnostic complex biomarkers or short screening tests from comprehensive test batteries. Thus, the ABC analysis can be proposed as a mathematically valid replacement for traditional limits to maximize the information obtained from multivariate research data.
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