ArticleScientific reports2017
Multiple similarly effective solutions exist for biomedical feature selection and classification problems.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- A Machine Learning-Based Investigation of Gender-Specific Prognosis of Lung Cancers.Medicina (Kaunas, Lithuania) · 2021Article
- Feature Selection of OMIC Data by Ensemble Swarm Intelligence Based Approaches.Frontiers in genetics · 2021Article
- MuscNet, a Weighted Voting Model of Multi-Source Connectivity Networks to Predict Mild Cognitive Impairment Using Resting-State Functional MRI.IEEE access : practical innovations, open solutions · 2020Article
- Robust clinical marker identification for diabetic kidney disease with ensemble feature selection.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Age Is Important for the Early-Stage Detection of Breast Cancer on Both Transcriptomic and Methylomic Biomarkers.Frontiers in genetics · 2019Article
- Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances.Journal of visualized experiments : JoVE · 2018Article
- Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis.International journal of molecular sciences · 2018Review
- Semi-Supervised Maximum Discriminative Local Margin for Gene Selection.Scientific reports · 2018Article
Corrections and comments
- Erratum issued
- Erratum issued
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Binary classification is a widely employed problem to facilitate the decisions on various biomedical big data questions, such as clinical drug trials between treated participants and controls, and genome-wide association studies (GWASs) between participants with or without a phenotype. A machine learning model is trained for this purpose by optimizing the power of discriminating samples from two groups. However, most of the classification algorithms tend to generate one locally optimal solution according to the input dataset and the mathematical presumptions of the dataset. Here we demonstrated from the aspects of both disease classification and feature selection that multiple different solutions may have similar classification performances. So the existing machine learning algorithms may have ignored a horde of fishes by catching only a good one. Since most of the existing machine learning algorithms generate a solution by optimizing a mathematical goal, it may be essential for understanding the biological mechanisms for the investigated classification question, by considering both the generated solution and the ignored ones.
Identifiers
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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.