M3S-GRPred: a novel ensemble learning approach for the interpretable prediction of glucocorticoid receptor antagonists using a multi-step stacking strategy

Accelerating drug discovery for glucocorticoid receptor (GR)-related disorders, including innovative machine learning (ML)-based approaches, holds promise in advancing therapeutic development, optimizing treatment efficacy, and mitigating adverse effects. While experimental methods can accurately id...

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Veröffentlicht in:	BMC bioinformatics Jg. 26; H. 1; S. 117 - 24
Hauptverfasser:	Schaduangrat, Nalini, Chuntakaruk, Hathaichanok, Rungrotmongkol, Thanyada, Mookdarsanit, Pakpoom, Shoombuatong, Watshara
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 30.04.2025 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Algorithms Analysis Bioinformatics Bioinformatics and chemoinformatics in drug discovery Biomedical and Life Sciences Cheminformatics Computational Biology - methods Computational Biology/Bioinformatics Computer Appl. in Life Sciences Cost effectiveness Cushing syndrome Cushing’s syndrome Datasets Diabetes Drug development Drug discovery Drug Discovery - methods Drug therapy Ensemble Learning Experimental methods FDA approval Feature selection Glucocorticoid receptor Glucocorticoid receptors Glucocorticoids Hormones Humans Life Sciences Machine Learning Methods Microarrays Molecular docking Mortality Physiological aspects QSAR R&D Receptors Receptors, Glucocorticoid - antagonists & inhibitors Receptors, Glucocorticoid - chemistry Research & development Surgery Transcription factors Thailand Cheminformatics Feature selection QSAR Multi-view feature Glucocorticoid receptor Machine learning Cushing’s syndrome
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
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