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algorithm python » algorithms within (Expand Search)
within function » fibrin function (Expand Search), protein function (Expand Search), catenin function (Expand Search)
python function » protein function (Expand Search)
algorithm both » algorithm blood (Expand Search), algorithm b (Expand Search), algorithm etc (Expand Search)
both function » body function (Expand Search), growth function (Expand Search), beach function (Expand Search)
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801
Gene expression feature importance ranking.
Published 2025“…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”
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802
BP schematic diagram.
Published 2025“…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”
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803
DNAmethylationfeature importance ranking.
Published 2025“…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”
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804
Pearson correlation heatmap of DNAmethylation.
Published 2025“…We performed comparative analyses against other methodologies across various functions and public datasets to assess their effectiveness. …”
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805
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806
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807
Right Knee fNIRS MI Dataset
Published 2025“…<p><br></p><p dir="ltr">This dataset contains functional near-infrared spectroscopy (fNIRS) signals recorded during motor imagery (MI) tasks of lower limb movements. …”
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808
Left Knee fNIRS MI Dataset
Published 2025“…<p><br></p><p dir="ltr">This dataset contains functional near-infrared spectroscopy (fNIRS) signals recorded during motor imagery (MI) tasks of lower limb movements. …”
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809
Left Ankle fNIRS MI Dataset
Published 2025“…<p><br></p><p dir="ltr">This dataset contains functional near-infrared spectroscopy (fNIRS) signals recorded during motor imagery (MI) tasks of lower limb movements. …”
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810
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811
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812
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813
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814
Two datasets.
Published 2024“…<div><p>This article examines the estimate of <i>ϑ</i> = <i>P</i> [<i>T</i> < <i>Q</i>], using both Bayesian and non-Bayesian methods, utilizing progressively first-failure censored data. …”
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815
Histogram of MCMC estimates of <i>ϑ</i>.
Published 2024“…<div><p>This article examines the estimate of <i>ϑ</i> = <i>P</i> [<i>T</i> < <i>Q</i>], using both Bayesian and non-Bayesian methods, utilizing progressively first-failure censored data. …”
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816
Trace plot of MCMC estimates of <i>ϑ</i>.
Published 2024“…<div><p>This article examines the estimate of <i>ϑ</i> = <i>P</i> [<i>T</i> < <i>Q</i>], using both Bayesian and non-Bayesian methods, utilizing progressively first-failure censored data. …”
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817
The POFIF-C samples from the real data sets.
Published 2024“…<div><p>This article examines the estimate of <i>ϑ</i> = <i>P</i> [<i>T</i> < <i>Q</i>], using both Bayesian and non-Bayesian methods, utilizing progressively first-failure censored data. …”
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818
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819
General Chemically Intuitive Atom- and Bond-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction
Published 2025“…Remarkably, the best performing neural network trained on hybrid embeddings outperforms the best purely structural model investigated despite the latter benefiting from of an embedding strategy with 267 times more data points than the one used for generating and embedding hybrid descriptors, with both strategies being unsupervised learning algorithms that share considerable conceptual and architectural similarities.…”
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820
General Chemically Intuitive Atom- and Bond-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction
Published 2025“…Remarkably, the best performing neural network trained on hybrid embeddings outperforms the best purely structural model investigated despite the latter benefiting from of an embedding strategy with 267 times more data points than the one used for generating and embedding hybrid descriptors, with both strategies being unsupervised learning algorithms that share considerable conceptual and architectural similarities.…”