ArticlePloS one2015
DWFS: a wrapper feature selection tool based on a parallel genetic algorithm.
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 24 papers.
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Who cites it
24 citing papers in PubMed.
- Article
- LOCATOR: feature extraction and spatial analysis of the cancer tissue microenvironment using mass cytometry imaging technologies.Bioinformatics advances · 2023Article
- Hybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification.Medical & biological engineering & computing · 2022Article
- Feature selection revisited in the single-cell era.Genome biology · 2021Review
- TopoFun: a machine learning method to improve the functional similarity of gene co-expression modules.NAR genomics and bioinformatics · 2021Article
- Cancer gene expression profiles associated with clinical outcomes to chemotherapy treatments.BMC medical genomics · 2020Article
- DSPLMF: A Method for Cancer Drug Sensitivity Prediction Using a Novel Regularization Approach in Logistic Matrix Factorization.Frontiers in genetics · 2020Article
- Evaluating the consistency of large-scale pharmacogenomic studies.Briefings in bioinformatics · 2019Review
- FeatureSelect: a software for feature selection based on machine learning approaches.BMC bioinformatics · 2019Article
- Autoencoder Based Feature Selection Method for Classification of Anticancer Drug Response.Frontiers in genetics · 2019Article
- New Paradigm of Machine Learning (ML) in Personalized Oncology: Data Trimming for Squeezing More Biomarkers From Clinical Datasets.Frontiers in oncology · 2019Article
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- Article
- TELS: A Novel Computational Framework for Identifying Motif Signatures of Transcribed Enhancers.Genomics, proteomics & bioinformatics · 2018Article
- DPubChem: a web tool for QSAR modeling and high-throughput virtual screening.Scientific reports · 2018Article
- FLOating-Window Projective Separator (FloWPS): A Data Trimming Tool for Support Vector Machines (SVM) to Improve Robustness of the Classifier.Frontiers in genetics · 2018Article
- Estimation of start and stop numbers for cluster resolution feature selection algorithm: an empirical approach using null distribution analysis of Fisher ratios.Analytical and bioanalytical chemistry · 2017Article
- Discriminative identification of transcriptional responses of promoters and enhancers after stimulus.Nucleic acids research · 2017Article
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- Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Many scientific problems can be formulated as classification tasks. Data that harbor relevant information are usually described by a large number of features. Frequently, many of these features are irrelevant for the class prediction. The efficient implementation of classification models requires identification of suitable combinations of features. The smaller number of features reduces the problem's dimensionality and may result in higher classification performance. We developed DWFS, a web-based tool that allows for efficient selection of features for a variety of problems. DWFS follows the wrapper paradigm and applies a search strategy based on Genetic Algorithms (GAs). A parallel GA implementation examines and evaluates simultaneously large number of candidate collections of features. DWFS also integrates various filtering methods that may be applied as a pre-processing step in the feature selection process. Furthermore, weights and parameters in the fitness function of GA can be adjusted according to the application requirements. Experiments using heterogeneous datasets from different biomedical applications demonstrate that DWFS is fast and leads to a significant reduction of the number of features without sacrificing performance as compared to several widely used existing methods. DWFS can be accessed online at www.cbrc.kaust.edu.sa/dwfs.
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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.