ArticleScientific reports2021
A machine learning method based on the genetic and world competitive contests algorithms for selecting genes or features in biological applications.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- An interpretable deep learning framework uncovers features governing CRISPR-Cas9 genome-editing efficiency.Bioinformatics (Oxford, England) · 2026Article
- Emerging advancements and expanding technological scope of education and practices in pharmacy and pharmaceutical sciences.BioImpacts : BI · 2026Review
- Establishment of a Predictive Model for Mortality in Sepsis Patients Using WGCNA and Machine Learning Algorithms.Journal of inflammation research · 2026Article
- Fire susceptibility assessment in the Carpathians using an interpretable framework.Scientific reports · 2025Article
- Utilization of Computable Phenotypes in Electronic Health Record Research: A Review and Case Study in Atopic Dermatitis.The Journal of investigative dermatology · 2025Review
- Transforming Cancer Classification: The Role of Advanced Gene Selection.Diagnostics (Basel, Switzerland) · 2024Article
- A voting-based machine learning approach for classifying biological and clinical datasets.BMC bioinformatics · 2023Article
- Article
- Structure-based drug repurposing against COVID-19 and emerging infectious diseases: methods, resources and discoveries.Briefings in bioinformatics · 2021Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gene/feature selection is an essential preprocessing step for creating models using machine learning techniques. It also plays a critical role in different biological applications such as the identification of biomarkers. Although many feature/gene selection algorithms and methods have been introduced, they may suffer from problems such as parameter tuning or low level of performance. To tackle such limitations, in this study, a universal wrapper approach is introduced based on our introduced optimization algorithm and the genetic algorithm (GA). In the proposed approach, candidate solutions have variable lengths, and a support vector machine scores them. To show the usefulness of the method, thirteen classification and regression-based datasets with different properties were chosen from various biological scopes, including drug discovery, cancer diagnostics, clinical applications, etc. Our findings confirmed that the proposed method outperforms most of the other currently used approaches and can also free the users from difficulties related to the tuning of various parameters. As a result, users may optimize their biological applications such as obtaining a biomarker diagnostic kit with the minimum number of genes and maximum separability power.
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