Search alternatives:
network optimization » swarm optimization (Expand Search), wolf optimization (Expand Search)
driven optimization » design optimization (Expand Search), guided optimization (Expand Search), dose optimization (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
genes based » gene based (Expand Search), lens based (Expand Search)
network optimization » swarm optimization (Expand Search), wolf optimization (Expand Search)
driven optimization » design optimization (Expand Search), guided optimization (Expand Search), dose optimization (Expand Search)
binary data » primary data (Expand Search), dietary data (Expand Search)
genes based » gene based (Expand Search), lens based (Expand Search)
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Flow diagram of the proposed model.
Published 2025“…<div><p>Machine learning models are increasingly applied to assisted reproductive technologies (ART), yet most studies rely on conventional algorithms with limited optimization. This proof-of-concept study investigates whether a hybrid Logistic Regression–Artificial Bee Colony (LR–ABC) framework can enhance predictive performance in in vitro fertilization (IVF) outcomes while producing interpretable, hypothesis-driven associations with nutritional and pharmaceutical supplement use. …”
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Results of the BRIDES node selection procedure for two scenarios and three weighted models.
Published 2022Subjects: -
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Table3_Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers.xlsx
Published 2023“…Additional regulators of the identified signature were discovered using Clarivate CBDD (Computational Biology for Drug Discovery) network propagation and hidden nodes algorithms on a curated network of molecular interactions (MetaBase™). …”
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Table1_Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers.xlsx
Published 2023“…Additional regulators of the identified signature were discovered using Clarivate CBDD (Computational Biology for Drug Discovery) network propagation and hidden nodes algorithms on a curated network of molecular interactions (MetaBase™). …”
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Table2_Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers.XLSX
Published 2023“…Additional regulators of the identified signature were discovered using Clarivate CBDD (Computational Biology for Drug Discovery) network propagation and hidden nodes algorithms on a curated network of molecular interactions (MetaBase™). …”
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Image1_Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers.JPEG
Published 2023“…Additional regulators of the identified signature were discovered using Clarivate CBDD (Computational Biology for Drug Discovery) network propagation and hidden nodes algorithms on a curated network of molecular interactions (MetaBase™). …”
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