xgboost Search Results


gradient boosted trees xgboost framework  (Merck KGaA)
90
Merck KGaA gradient boosted trees xgboost framework
Gradient Boosted Trees Xgboost Framework, supplied by Merck KGaA, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/gradient boosted trees xgboost framework/product/Merck KGaA
Average 90 stars, based on 1 article reviews
gradient boosted trees xgboost framework - by Bioz Stars, 2026-06
90/100 stars
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xgboost  (Anwendung GmbH)
90
Anwendung GmbH xgboost
Xgboost, supplied by Anwendung GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/xgboost/product/Anwendung GmbH
Average 90 stars, based on 1 article reviews
xgboost - by Bioz Stars, 2026-06
90/100 stars
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xgboost  (Nextup Technologies)
90
Nextup Technologies xgboost
Xgboost, supplied by Nextup Technologies, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/xgboost/product/Nextup Technologies
Average 90 stars, based on 1 article reviews
xgboost - by Bioz Stars, 2026-06
90/100 stars
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xgboost cem 0.728  (CEM Corporation)
90
CEM Corporation xgboost cem 0.728
Xgboost Cem 0.728, supplied by CEM Corporation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/xgboost cem 0.728/product/CEM Corporation
Average 90 stars, based on 1 article reviews
xgboost cem 0.728 - by Bioz Stars, 2026-06
90/100 stars
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xgboost based on discrete wavelet transform  (DEStech Publications)
90
DEStech Publications xgboost based on discrete wavelet transform
Xgboost Based On Discrete Wavelet Transform, supplied by DEStech Publications, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/xgboost based on discrete wavelet transform/product/DEStech Publications
Average 90 stars, based on 1 article reviews
xgboost based on discrete wavelet transform - by Bioz Stars, 2026-06
90/100 stars
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xgboost analyses  (RStudio)
90
RStudio xgboost analyses
Examined and optimized hyperparameters for average speed <t> XGBoost </t> algorithms.
Xgboost Analyses, supplied by RStudio, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/xgboost analyses/product/RStudio
Average 90 stars, based on 1 article reviews
xgboost analyses - by Bioz Stars, 2026-06
90/100 stars
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xgboost model  (MedCalc Software Ltd)
90
MedCalc Software Ltd xgboost model
Examined and optimized hyperparameters for average speed <t> XGBoost </t> algorithms.
Xgboost Model, supplied by MedCalc Software Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/xgboost model/product/MedCalc Software Ltd
Average 90 stars, based on 1 article reviews
xgboost model - by Bioz Stars, 2026-06
90/100 stars
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xgboost  (KNIME GmbH)
90
KNIME GmbH xgboost
Examined and optimized hyperparameters for average speed <t> XGBoost </t> algorithms.
Xgboost, supplied by KNIME GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/xgboost/product/KNIME GmbH
Average 90 stars, based on 1 article reviews
xgboost - by Bioz Stars, 2026-06
90/100 stars
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xgboost  (Dynomics Inc)
90
Dynomics Inc xgboost
Radiomics Workflow. Beginning with the acquisition of medical images, volumes of interest (VOIs) are manually segmented on both the lesions and a healthy reference tissue. Radiomic features are subsequently extracted from these VOIs and superimposed onto the static 18 F-FLT PET image (a 3D image derived from averaging the final five time-frames of the dynamic acquisition), thus establishing the basis for static radiomics. For our novel dynomics approach, these features are extrapolated from each frame of the dynamic 18 F-FLT PET acquisition. In this process, summary values—including median and median absolute deviation (MAD)—are evaluated for each feature and analyzed in conjunction with their temporal evolution (dynamic features). The features encapsulate information about the tumor’s shape, first-order statistical features (derived from the image intensity histogram), and second-order statistical features (texture features). To optimize the data for interpretation, radiomics features undergo redundancy correction via principal component analysis (PCA), enabling the analysis of only non-redundant, meaningful features. These streamlined features are then processed through a machine learning model <t>(XGBoost),</t> generating a clinically interpretable outcome (lesion vs. reference tissue and complete vs. partial responders’ classification).
Xgboost, supplied by Dynomics Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/xgboost/product/Dynomics Inc
Average 90 stars, based on 1 article reviews
xgboost - by Bioz Stars, 2026-06
90/100 stars
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ml-xgboost  (Gauch GmbH)
90
Gauch GmbH ml-xgboost
Radiomics Workflow. Beginning with the acquisition of medical images, volumes of interest (VOIs) are manually segmented on both the lesions and a healthy reference tissue. Radiomic features are subsequently extracted from these VOIs and superimposed onto the static 18 F-FLT PET image (a 3D image derived from averaging the final five time-frames of the dynamic acquisition), thus establishing the basis for static radiomics. For our novel dynomics approach, these features are extrapolated from each frame of the dynamic 18 F-FLT PET acquisition. In this process, summary values—including median and median absolute deviation (MAD)—are evaluated for each feature and analyzed in conjunction with their temporal evolution (dynamic features). The features encapsulate information about the tumor’s shape, first-order statistical features (derived from the image intensity histogram), and second-order statistical features (texture features). To optimize the data for interpretation, radiomics features undergo redundancy correction via principal component analysis (PCA), enabling the analysis of only non-redundant, meaningful features. These streamlined features are then processed through a machine learning model <t>(XGBoost),</t> generating a clinically interpretable outcome (lesion vs. reference tissue and complete vs. partial responders’ classification).
Ml Xgboost, supplied by Gauch GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/ml-xgboost/product/Gauch GmbH
Average 90 stars, based on 1 article reviews
ml-xgboost - by Bioz Stars, 2026-06
90/100 stars
  Buy from Supplier
xgboost model  (Arsia Therapeutics)
90
Arsia Therapeutics xgboost model
Radiomics Workflow. Beginning with the acquisition of medical images, volumes of interest (VOIs) are manually segmented on both the lesions and a healthy reference tissue. Radiomic features are subsequently extracted from these VOIs and superimposed onto the static 18 F-FLT PET image (a 3D image derived from averaging the final five time-frames of the dynamic acquisition), thus establishing the basis for static radiomics. For our novel dynomics approach, these features are extrapolated from each frame of the dynamic 18 F-FLT PET acquisition. In this process, summary values—including median and median absolute deviation (MAD)—are evaluated for each feature and analyzed in conjunction with their temporal evolution (dynamic features). The features encapsulate information about the tumor’s shape, first-order statistical features (derived from the image intensity histogram), and second-order statistical features (texture features). To optimize the data for interpretation, radiomics features undergo redundancy correction via principal component analysis (PCA), enabling the analysis of only non-redundant, meaningful features. These streamlined features are then processed through a machine learning model <t>(XGBoost),</t> generating a clinically interpretable outcome (lesion vs. reference tissue and complete vs. partial responders’ classification).
Xgboost Model, supplied by Arsia Therapeutics, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/xgboost model/product/Arsia Therapeutics
Average 90 stars, based on 1 article reviews
xgboost model - by Bioz Stars, 2026-06
90/100 stars
  Buy from Supplier
xgboost  (Beijing Science and Technology Co Ltd)
90
Beijing Science and Technology Co Ltd xgboost
Radiomics Workflow. Beginning with the acquisition of medical images, volumes of interest (VOIs) are manually segmented on both the lesions and a healthy reference tissue. Radiomic features are subsequently extracted from these VOIs and superimposed onto the static 18 F-FLT PET image (a 3D image derived from averaging the final five time-frames of the dynamic acquisition), thus establishing the basis for static radiomics. For our novel dynomics approach, these features are extrapolated from each frame of the dynamic 18 F-FLT PET acquisition. In this process, summary values—including median and median absolute deviation (MAD)—are evaluated for each feature and analyzed in conjunction with their temporal evolution (dynamic features). The features encapsulate information about the tumor’s shape, first-order statistical features (derived from the image intensity histogram), and second-order statistical features (texture features). To optimize the data for interpretation, radiomics features undergo redundancy correction via principal component analysis (PCA), enabling the analysis of only non-redundant, meaningful features. These streamlined features are then processed through a machine learning model <t>(XGBoost),</t> generating a clinically interpretable outcome (lesion vs. reference tissue and complete vs. partial responders’ classification).
Xgboost, supplied by Beijing Science and Technology Co Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/result/xgboost/product/Beijing Science and Technology Co Ltd
Average 90 stars, based on 1 article reviews
xgboost - by Bioz Stars, 2026-06
90/100 stars
  Buy from Supplier

Image Search Results


Examined and optimized hyperparameters for average speed XGBoost algorithms.

Journal: Journal of Safety Research

Article Title: Identifying the impact of the COVID-19 pandemic on driving behavior using naturalistic driving data and time series forecasting

doi: 10.1016/j.jsr.2021.04.007

Figure Lengend Snippet: Examined and optimized hyperparameters for average speed XGBoost algorithms.

Article Snippet: All XGBoost analyses were conducted in R-studio ( ).

Techniques:

Feature importance of average speed XGBoost algorithms.

Journal: Journal of Safety Research

Article Title: Identifying the impact of the COVID-19 pandemic on driving behavior using naturalistic driving data and time series forecasting

doi: 10.1016/j.jsr.2021.04.007

Figure Lengend Snippet: Feature importance of average speed XGBoost algorithms.

Article Snippet: All XGBoost analyses were conducted in R-studio ( ).

Techniques:

Examined and optimized hyperparameters for speeding XGBoost algorithms.

Journal: Journal of Safety Research

Article Title: Identifying the impact of the COVID-19 pandemic on driving behavior using naturalistic driving data and time series forecasting

doi: 10.1016/j.jsr.2021.04.007

Figure Lengend Snippet: Examined and optimized hyperparameters for speeding XGBoost algorithms.

Article Snippet: All XGBoost analyses were conducted in R-studio ( ).

Techniques:

Feature importance of speeding XGBoost algorithms.

Journal: Journal of Safety Research

Article Title: Identifying the impact of the COVID-19 pandemic on driving behavior using naturalistic driving data and time series forecasting

doi: 10.1016/j.jsr.2021.04.007

Figure Lengend Snippet: Feature importance of speeding XGBoost algorithms.

Article Snippet: All XGBoost analyses were conducted in R-studio ( ).

Techniques:

Examined and optimized hyperparameters for harsh braking/100 km XGBoost algorithms.

Journal: Journal of Safety Research

Article Title: Identifying the impact of the COVID-19 pandemic on driving behavior using naturalistic driving data and time series forecasting

doi: 10.1016/j.jsr.2021.04.007

Figure Lengend Snippet: Examined and optimized hyperparameters for harsh braking/100 km XGBoost algorithms.

Article Snippet: All XGBoost analyses were conducted in R-studio ( ).

Techniques:

Feature importance of harsh braking/100 km XGBoost algorithms.

Journal: Journal of Safety Research

Article Title: Identifying the impact of the COVID-19 pandemic on driving behavior using naturalistic driving data and time series forecasting

doi: 10.1016/j.jsr.2021.04.007

Figure Lengend Snippet: Feature importance of harsh braking/100 km XGBoost algorithms.

Article Snippet: All XGBoost analyses were conducted in R-studio ( ).

Techniques:

Radiomics Workflow. Beginning with the acquisition of medical images, volumes of interest (VOIs) are manually segmented on both the lesions and a healthy reference tissue. Radiomic features are subsequently extracted from these VOIs and superimposed onto the static 18 F-FLT PET image (a 3D image derived from averaging the final five time-frames of the dynamic acquisition), thus establishing the basis for static radiomics. For our novel dynomics approach, these features are extrapolated from each frame of the dynamic 18 F-FLT PET acquisition. In this process, summary values—including median and median absolute deviation (MAD)—are evaluated for each feature and analyzed in conjunction with their temporal evolution (dynamic features). The features encapsulate information about the tumor’s shape, first-order statistical features (derived from the image intensity histogram), and second-order statistical features (texture features). To optimize the data for interpretation, radiomics features undergo redundancy correction via principal component analysis (PCA), enabling the analysis of only non-redundant, meaningful features. These streamlined features are then processed through a machine learning model (XGBoost), generating a clinically interpretable outcome (lesion vs. reference tissue and complete vs. partial responders’ classification).

Journal: Journal of Personalized Medicine

Article Title: Dynomics: A Novel and Promising Approach for Improved Breast Cancer Prognosis Prediction

doi: 10.3390/jpm13061004

Figure Lengend Snippet: Radiomics Workflow. Beginning with the acquisition of medical images, volumes of interest (VOIs) are manually segmented on both the lesions and a healthy reference tissue. Radiomic features are subsequently extracted from these VOIs and superimposed onto the static 18 F-FLT PET image (a 3D image derived from averaging the final five time-frames of the dynamic acquisition), thus establishing the basis for static radiomics. For our novel dynomics approach, these features are extrapolated from each frame of the dynamic 18 F-FLT PET acquisition. In this process, summary values—including median and median absolute deviation (MAD)—are evaluated for each feature and analyzed in conjunction with their temporal evolution (dynamic features). The features encapsulate information about the tumor’s shape, first-order statistical features (derived from the image intensity histogram), and second-order statistical features (texture features). To optimize the data for interpretation, radiomics features undergo redundancy correction via principal component analysis (PCA), enabling the analysis of only non-redundant, meaningful features. These streamlined features are then processed through a machine learning model (XGBoost), generating a clinically interpretable outcome (lesion vs. reference tissue and complete vs. partial responders’ classification).

Article Snippet: , Dynomics—Median , XGBoost , 0.67 , 0.71 , 1.00 , 0.67 , 0.33 , 1.00.

Techniques: Derivative Assay

Summary of model performances when discriminating tumors from the reference tissue using static and dynamic radiomic features and images.

Journal: Journal of Personalized Medicine

Article Title: Dynomics: A Novel and Promising Approach for Improved Breast Cancer Prognosis Prediction

doi: 10.3390/jpm13061004

Figure Lengend Snippet: Summary of model performances when discriminating tumors from the reference tissue using static and dynamic radiomic features and images.

Article Snippet: , Dynomics—Median , XGBoost , 0.67 , 0.71 , 1.00 , 0.67 , 0.33 , 1.00.

Techniques:

Summary of model performance when discriminating complete from partial responders using static and dynamic radiomic features and images.

Journal: Journal of Personalized Medicine

Article Title: Dynomics: A Novel and Promising Approach for Improved Breast Cancer Prognosis Prediction

doi: 10.3390/jpm13061004

Figure Lengend Snippet: Summary of model performance when discriminating complete from partial responders using static and dynamic radiomic features and images.

Article Snippet: , Dynomics—Median , XGBoost , 0.67 , 0.71 , 1.00 , 0.67 , 0.33 , 1.00.

Techniques: