ArticlePeerJ2022
ExhauFS: exhaustive search-based feature selection for classification and survival regression.
Article in PeerJ, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Circulating miRNA profiles in COVID-19 patients and meta-analysis: implications for disease progression and prognosis.Scientific reports · 2023Pooled it
- Enhanced generalized normal distribution optimizer with Gaussian distribution repair method and cauchy reverse learning for features selection.Scientific reports · 2026Article
- Breast Cancer Prediction Using Rotation Forest Algorithm Along with Finding the Influential Causes.Bioengineering (Basel, Switzerland) · 2025Article
- A novel approach for a joint analysis of isomiR and mRNA expression data reveals features of isomiR targeting in breast cancer.Frontiers in genetics · 2022Article
- ECM-Receptor Regulatory Network and Its Prognostic Role in Colorectal Cancer.Frontiers in genetics · 2021Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Feature selection is one of the main techniques used to prevent overfitting in machine learning applications. The most straightforward approach for feature selection is an exhaustive search: one can go over all possible feature combinations and pick up the model with the highest accuracy. This method together with its optimizations were actively used in biomedical research, however, publicly available implementation is missing. We present ExhauFS-the user-friendly command-line implementation of the exhaustive search approach for classification and survival regression. Aside from tool description, we included three application examples in the manuscript to comprehensively review the implemented functionality. First, we executed ExhauFS on a toy cervical cancer dataset to illustrate basic concepts. Then, multi-cohort microarray breast cancer datasets were used to construct gene signatures for 5-year recurrence classification. The vast majority of signatures constructed by ExhauFS passed 0.65 threshold of sensitivity and specificity on all datasets, including the validation one. Moreover, a number of gene signatures demonstrated reliable performance on independent RNA-seq dataset without any coefficient re-tuning,
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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.