بدائل البحث:
driven optimization » design optimization (توسيع البحث), guided optimization (توسيع البحث), dose optimization (توسيع البحث)
joint optimization » policy optimization (توسيع البحث), wolf optimization (توسيع البحث), codon optimization (توسيع البحث)
primary data » primary care (توسيع البحث)
three joint » three point (توسيع البحث), three points (توسيع البحث), three coping (توسيع البحث)
driven optimization » design optimization (توسيع البحث), guided optimization (توسيع البحث), dose optimization (توسيع البحث)
joint optimization » policy optimization (توسيع البحث), wolf optimization (توسيع البحث), codon optimization (توسيع البحث)
primary data » primary care (توسيع البحث)
three joint » three point (توسيع البحث), three points (توسيع البحث), three coping (توسيع البحث)
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Data used to drive the Double Layer Carbon Model in the Qinling Mountains.
منشور في 2024"…The model divides the soil profile into topsoil (0-20 cm) and subsoil (20–100 cm) layers to match the SOC maps of the corresponding two layers generated by data-driven models. Each of these layers contains a young carbon pool (CY) with a higher decomposition rate and an old carbon pool (CO) with a lower decomposition rate. …"
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Label-Free Assessment of the Drug Resistance of Epithelial Ovarian Cancer Cells in a Microfluidic Holographic Flow Cytometer Boosted through Machine Learning
منشور في 2021"…Furthermore, it reflects strong potentialities to develop data-driven individualized chemotherapy treatments in the future.…"
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Label-Free Assessment of the Drug Resistance of Epithelial Ovarian Cancer Cells in a Microfluidic Holographic Flow Cytometer Boosted through Machine Learning
منشور في 2021"…Furthermore, it reflects strong potentialities to develop data-driven individualized chemotherapy treatments in the future.…"
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Variable Selection with Multiply-Imputed Datasets: Choosing Between Stacked and Grouped Methods
منشور في 2022"…Building on existing work, we (i) derive and implement efficient cyclic coordinate descent and majorization-minimization optimization algorithms for continuous and binary outcome data, (ii) incorporate adaptive shrinkage penalties, (iii) compare these methods through simulation, and (iv) develop an R package <i>miselect</i>. …"
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Table_1_A Phenotyping of Diastolic Function by Machine Learning Improves Prediction of Clinical Outcomes in Heart Failure.DOCX
منشور في 2021"…</p><p>Conclusion: Machine learning can identify patterns of diastolic function that better stratify the risk for decompensation than the current consensus recommendations in HF. Integrating this data-driven phenotyping may help in refining prognostication and optimizing treatment.…"