Comparison of average reward values in various simulation scenarios. حسب Honglei Pang (22693724)

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HIL test results of split-μ road: (a)Vehicle velocity; (b) Braking distance; (c) Lateral displacement; (d) Lateral acceleration; (e)Vehicle sideslip angle; (f) Yaw rate. حسب Honglei Pang (22693724)

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AI-Assisted Label-Free Monitoring Bone Mineral Metabolism on Demineralized Bone Paper حسب Patrick Ryan (532054)

"…Here, we present a label-free, longitudinal, and quantitative monitoring of mineralized collagen formation by osteoblasts and subsequent osteoclast-driven mineral resorption on DBP using brightfield microscopy. A Segment.ai machine learning algorithm is applied for time-lapse bright-field image analysis, enabling identification of osteoclast resorption areas and automated quantification of large image datasets over a three-week culture period. …"

AI-Assisted Label-Free Monitoring Bone Mineral Metabolism on Demineralized Bone Paper حسب Patrick Ryan (532054)

"…Here, we present a label-free, longitudinal, and quantitative monitoring of mineralized collagen formation by osteoblasts and subsequent osteoclast-driven mineral resorption on DBP using brightfield microscopy. A Segment.ai machine learning algorithm is applied for time-lapse bright-field image analysis, enabling identification of osteoclast resorption areas and automated quantification of large image datasets over a three-week culture period. …"

AI-Assisted Label-Free Monitoring Bone Mineral Metabolism on Demineralized Bone Paper حسب Patrick Ryan (532054)

"…Here, we present a label-free, longitudinal, and quantitative monitoring of mineralized collagen formation by osteoblasts and subsequent osteoclast-driven mineral resorption on DBP using brightfield microscopy. A Segment.ai machine learning algorithm is applied for time-lapse bright-field image analysis, enabling identification of osteoclast resorption areas and automated quantification of large image datasets over a three-week culture period. …"

AI-Assisted Label-Free Monitoring Bone Mineral Metabolism on Demineralized Bone Paper حسب Patrick Ryan (532054)

"…Here, we present a label-free, longitudinal, and quantitative monitoring of mineralized collagen formation by osteoblasts and subsequent osteoclast-driven mineral resorption on DBP using brightfield microscopy. A Segment.ai machine learning algorithm is applied for time-lapse bright-field image analysis, enabling identification of osteoclast resorption areas and automated quantification of large image datasets over a three-week culture period. …"

PyNoetic’s classification module supports a range of popular and widely used ML classification models with just a single line of function call. حسب Gursimran Singh (575288)

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BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data حسب Jean-Christophe Lachance (6619307)

"…Despite its importance, no standardized computational platform is currently available to generate species-specific biomass objective functions in a data-driven, unbiased fashion. To fill this gap in the metabolic modeling software ecosystem, we implemented BOFdat, a Python package for the definition of a <b>B</b>iomass <b>O</b>bjective <b>F</b>unction from experimental <b>dat</b>a. …"

Plot depicting the feature importance of the top 15 features of each model. حسب Aastha Vatsyayan (9981916)

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Scatterplot of class probability vs the predicted class for the XGBoost model across all VUS variants 251 exonic VUS variants that were a part of the validation dataset. حسب Aastha Vatsyayan (9981916)

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Copper exposure in <i>ATP7B</i> Knock-out HepG2 cells overexpressed with the plasmid containing wildtype and mutant <i>ATP7B</i> gene. حسب Aastha Vatsyayan (9981916)

الموضوعات:

A complete list of the 73 features used in training the model, along with the ACMG attribute they provide information about, along with their description, as well as their datatype... حسب Aastha Vatsyayan (9981916)

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Scatterplot of class probability vs the actual ACMG class for the TabNet model across the validation set of 156 variants. حسب Aastha Vatsyayan (9981916)

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Model hyperparameters used for the XGBoost model. حسب Aastha Vatsyayan (9981916)

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The Precision-Recall curve of both the models. حسب Aastha Vatsyayan (9981916)

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S4 Table - حسب Aastha Vatsyayan (9981916)

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Train and validation accuracies of the TabNet model across 187 epochs. حسب Aastha Vatsyayan (9981916)

الموضوعات:

Primers used in site-directed mutagenesis. حسب Aastha Vatsyayan (9981916)

الموضوعات:

The overview of the workflow followed for model development and variant classification with TabNet and XGBoost models. حسب Aastha Vatsyayan (9981916)

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S2 Table - حسب Aastha Vatsyayan (9981916)

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