ArticleCancer informatics2011
A jackknife and voting classifier approach to feature selection and classification.
Article in Cancer informatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Machine Learning Algorithms for Classification of MALDI-TOF MS Spectra from Phylogenetically Closely Related SpeciesMicroorganisms · 2022Article
- An Integrative Computational Approach for the Prediction of Human-BioMed research international · 2020Article
- Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances.Journal of visualized experiments : JoVE · 2018Article
- Investigation of metabolomic blood biomarkers for detection of adenocarcinoma lung cancer.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2015Article
- Evaluation of glycomic profiling as a diagnostic biomarker for epithelial ovarian cancer.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2014Article
- Elevated TNFR1 and serotonin in bone metastasis are correlated with poor survival following bone metastasis diagnosis for both carcinoma and sarcoma primary tumors.Clinical cancer research : an official journal of the American Association for Cancer Research · 2013Article
- Gene expression profiles for predicting metastasis in breast cancer: a cross-study comparison of classification methods.TheScientificWorldJournal · 2012Article
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Authors and funding
2 authors.
Funding
Abstract
With technological advances now allowing measurement of thousands of genes, proteins and metabolites, researchers are using this information to develop diagnostic and prognostic tests and discern the biological pathways underlying diseases. Often, an investigator's objective is to develop a classification rule to predict group membership of unknown samples based on a small set of features and that could ultimately be used in a clinical setting. While common classification methods such as random forest and support vector machines are effective at separating groups, they do not directly translate into a clinically-applicable classification rule based on a small number of features.We present a simple feature selection and classification method for biomarker detection that is intuitively understandable and can be directly extended for application to a clinical setting. We first use a jackknife procedure to identify important features and then, for classification, we use voting classifiers which are simple and easy to implement. We compared our method to random forest and support vector machines using three benchmark cancer 'omics datasets with different characteristics. We found our jackknife procedure and voting classifier to perform comparably to these two methods in terms of accuracy. Further, the jackknife procedure yielded stable feature sets. Voting classifiers in combination with a robust feature selection method such as our jackknife procedure offer an effective, simple and intuitive approach to feature selection and classification with a clear extension to clinical applications.
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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.