A Comprehensive Evaluation Framework for Benchmarking Multi-Objective Feature Selection in Omics-Based Biomarker Discovery

Machine learning algorithms have been extensively used for accurate classification of cancer subtypes driven by gene expression-based biomarkers. However, biomarker models combining multiple gene expression signatures are often not reproducible in external validation datasets and their feature set s...

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Veröffentlicht in:	IEEE/ACM transactions on computational biology and bioinformatics Jg. 21; H. 6; S. 2432 - 2446
Hauptverfasser:	Cattelani, Luca, Ghosh, Arindam, Rintala, Teemu J., Fortino, Vittorio
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.11.2024
Schlagworte:	Accuracy Algorithms Benchmark testing Benchmarking Biological system modeling biology and genetics Biomarkers, Tumor - analysis Biomarkers, Tumor - genetics Biomarkers, Tumor - metabolism Cancer Classification algorithms Computational Biology - methods Databases, Genetic evolutionary computing and genetic algorithms Feature extraction Gene Expression Profiling - methods Genetic algorithms Humans Machine Learning Measurement Neoplasms - genetics Neoplasms - metabolism Prediction algorithms Training Transcriptome - genetics
ISSN:	1545-5963, 1557-9964, 1557-9964
Online-Zugang:	Volltext
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