Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Feature selection and classifier performance on diverse bio- logical datasets.

BMC bioinformatics - 1 Jan 2014

Hemphill Edward, Lindsay James, Lee Chih, Măndoiu Ion I, Nelson Craig E

Abstract excerpt

BACKGROUND: There is an ever-expanding range of technologies that generate very large numbers of biomarkers for research and clinical applications. Choosing the most informative biomarkers from a high-dimensional data set, combined with identifying the most reliable and accurate classification al...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Data Mining
Share to Databases, Factual

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to DNA Copy Number Variations

Share to MicroRNAs

Share to Oligonucleotide Array Sequence Analysis

Share to Proteins

Share to Tumor Cells, Cultured

Source

Open in PubMed Open full text

Identifiers

PMID: 25434802
DOI: 10.1186/1471-2105-15-S13-S4
PMCID: PMC4248652

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026. Prepared record publication-25434802-2026-09-04.2. Manifest publication-graph-cohort-2026-09-04.2.

Source receipt: PubMed via Helena Literature Corpus, retrieved 4 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Feature selection and classifier performance on diverse bio- logical datasets.PMID 25434802Read ↗

Utilizing Feature Selection Techniques for AI-Driven Tumor Subtype Classification: Enhancing Precision in Cancer DiagnosticsSemantic proximity Read ↗

Feature Selection and Molecular Classification of Cancer Phenotypes: A Comparative Study.Semantic proximity Read ↗

Efficient feature selection on gene expression data: Which algorithm to use?Semantic proximity Read ↗

Benchmarking 50 classification algorithms on 50 gene-expression datasetsSemantic proximity Read ↗

OmicSelector: automatic feature selection and deep learning modeling for omic experimentsSemantic proximity Read ↗

Using Machine Learning Algorithms to find Novel Biomarkers for Breast Cancer using RNA-Seq DatasetSemantic proximity Read ↗

Diagnostic biomarkers for renal cell carcinoma: selection using novel bioinformatics systems for microarray data analysisSemantic proximity Read ↗

Classification algorithms for phenotype prediction in genomics and proteomics.Semantic proximity Read ↗

Revisiting Logistic Regression for High-Dimensional Gene Expression DataSemantic proximity Read ↗

Automated Feature Selection and Classification for High-Dimensional Biomedical DataSemantic proximity Read ↗

ML Classification of Cancer Types Using High Dimensional Gene Expression Microarray DataSemantic proximity Read ↗

Biomarker identification by feature wrappers.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.