Review





Similar Products

pretrained model  (Oxford Nanopore)
90
Oxford Nanopore pretrained model
Pretrained Model, supplied by Oxford Nanopore, 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/pretrained model/product/Oxford Nanopore
Average 90 stars, based on 1 article reviews
pretrained model - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
pretrained cnn models  (Kaggle Inc)
90
Kaggle Inc pretrained cnn models
Pretrained Cnn Models, supplied by Kaggle 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/pretrained cnn models/product/Kaggle Inc
Average 90 stars, based on 1 article reviews
pretrained cnn models - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
pretrained model  (Bambu Vault LLC)
90
Bambu Vault LLC pretrained model
Pretrained Model, supplied by Bambu Vault LLC, 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/pretrained model/product/Bambu Vault LLC
Average 90 stars, based on 1 article reviews
pretrained model - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
pretrained cellplm model 20230926_85m  (Dropbox Inc)
90
Dropbox Inc pretrained cellplm model 20230926_85m
Pretrained Cellplm Model 20230926 85m, supplied by Dropbox 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/pretrained cellplm model 20230926_85m/product/Dropbox Inc
Average 90 stars, based on 1 article reviews
pretrained cellplm model 20230926_85m - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
pretrained dino models  (CEM Corporation)
90
CEM Corporation pretrained dino models
Overview of the Attention-Guided Erasing (AGE) Methodology. a Self-Supervised Pretraining using <t>DINO</t> [11]: A teacher student ViT framework, leveraging a teacher-student ViT-S self-distillation framework. b AGE [13]: Attention head visualizations from the SSL <t>pretrained</t> teacher ViT-S are converted into binary masks to isolate key ROIs and then used to erase background regions. c Transfer Learning with AGE: AGE is used on the input images using each of the attention heads with a random probability during training. The attention head yielding the highest validation performance is selected for final AGE-based transfer learning
Pretrained Dino Models, 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/pretrained dino models/product/CEM Corporation
Average 90 stars, based on 1 article reviews
pretrained dino models - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
pretrained models  (MathWorks Inc)
90
MathWorks Inc pretrained models
Overview of the Attention-Guided Erasing (AGE) Methodology. a Self-Supervised Pretraining using <t>DINO</t> [11]: A teacher student ViT framework, leveraging a teacher-student ViT-S self-distillation framework. b AGE [13]: Attention head visualizations from the SSL <t>pretrained</t> teacher ViT-S are converted into binary masks to isolate key ROIs and then used to erase background regions. c Transfer Learning with AGE: AGE is used on the input images using each of the attention heads with a random probability during training. The attention head yielding the highest validation performance is selected for final AGE-based transfer learning
Pretrained Models, supplied by MathWorks 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/pretrained models/product/MathWorks Inc
Average 90 stars, based on 1 article reviews
pretrained models - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
pretrained model gemma-7b  (Deepmind Technologies Ltd)
90
Deepmind Technologies Ltd pretrained model gemma-7b
Overview of the Attention-Guided Erasing (AGE) Methodology. a Self-Supervised Pretraining using <t>DINO</t> [11]: A teacher student ViT framework, leveraging a teacher-student ViT-S self-distillation framework. b AGE [13]: Attention head visualizations from the SSL <t>pretrained</t> teacher ViT-S are converted into binary masks to isolate key ROIs and then used to erase background regions. c Transfer Learning with AGE: AGE is used on the input images using each of the attention heads with a random probability during training. The attention head yielding the highest validation performance is selected for final AGE-based transfer learning
Pretrained Model Gemma 7b, supplied by Deepmind Technologies 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/pretrained model gemma-7b/product/Deepmind Technologies Ltd
Average 90 stars, based on 1 article reviews
pretrained model gemma-7b - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
kmer model pretrained on the sequel data  (Pacific Biosciences)
90
Pacific Biosciences kmer model pretrained on the sequel data
Overview of the Attention-Guided Erasing (AGE) Methodology. a Self-Supervised Pretraining using <t>DINO</t> [11]: A teacher student ViT framework, leveraging a teacher-student ViT-S self-distillation framework. b AGE [13]: Attention head visualizations from the SSL <t>pretrained</t> teacher ViT-S are converted into binary masks to isolate key ROIs and then used to erase background regions. c Transfer Learning with AGE: AGE is used on the input images using each of the attention heads with a random probability during training. The attention head yielding the highest validation performance is selected for final AGE-based transfer learning
Kmer Model Pretrained On The Sequel Data, supplied by Pacific Biosciences, 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/kmer model pretrained on the sequel data/product/Pacific Biosciences
Average 90 stars, based on 1 article reviews
kmer model pretrained on the sequel data - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
pretrained kmer model  (Pacific Biosciences)
90
Pacific Biosciences pretrained kmer model
A. Schematic for SMRT CCS and 6mA calling. B. Comparing ipdSummary and ipdTrimming pipelines for 6mA calling using the subread level Sequel data. C. Performance of ipdSummary and ipdTrimming on four different datasets as indicated on left. The thresholds for calling 6mA are indicated by black lines. For dam + plasmid DNA dataset, the thresholds for 98.5% 6mA recall are indicated by red lines. The same recall thresholds are used to calculate the background noise level for 6mA calling in the WGA dataset. False positive rates (FPR, grey shade) and false negative rates (FNR, red shade) for both Tetrahymena gDNA and human 6mA-FP samples were calculated after Gaussian demixing of IPDr distribution. D. Comparing the computational costs, including job all clock time, CPU running time and peak memory usage of ipdSummary and ipdTrimming. E. Comparing 6mA calling results by ipdTrimming and fibertools. Red curves represent IPDr distribution for all adenine sites calculated by ipdTrimming; blue curves represent the number of adenine sites with indicated IPDr values that are called as 6mA by fibertools. The thresholds for 6mA calling by ipdTrimming are indicated by black lines. Recall rates (black label) and FPR (red label) were calculated after Gaussian demixing of IPDr distribution. F. Comparing 6mA calling results for the Revio system, generated by the Sequel <t>kmer</t> model, human native gDNA control, and the Revio kmer model. FPR (grey shade) and FNR (red shade) were calculated after Gaussian demixing of IPDr distribution.
Pretrained Kmer Model, supplied by Pacific Biosciences, 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/pretrained kmer model/product/Pacific Biosciences
Average 90 stars, based on 1 article reviews
pretrained kmer model - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier
pretrained models galactica  (Galactica Pharma)
90
Galactica Pharma pretrained models galactica
A. Schematic for SMRT CCS and 6mA calling. B. Comparing ipdSummary and ipdTrimming pipelines for 6mA calling using the subread level Sequel data. C. Performance of ipdSummary and ipdTrimming on four different datasets as indicated on left. The thresholds for calling 6mA are indicated by black lines. For dam + plasmid DNA dataset, the thresholds for 98.5% 6mA recall are indicated by red lines. The same recall thresholds are used to calculate the background noise level for 6mA calling in the WGA dataset. False positive rates (FPR, grey shade) and false negative rates (FNR, red shade) for both Tetrahymena gDNA and human 6mA-FP samples were calculated after Gaussian demixing of IPDr distribution. D. Comparing the computational costs, including job all clock time, CPU running time and peak memory usage of ipdSummary and ipdTrimming. E. Comparing 6mA calling results by ipdTrimming and fibertools. Red curves represent IPDr distribution for all adenine sites calculated by ipdTrimming; blue curves represent the number of adenine sites with indicated IPDr values that are called as 6mA by fibertools. The thresholds for 6mA calling by ipdTrimming are indicated by black lines. Recall rates (black label) and FPR (red label) were calculated after Gaussian demixing of IPDr distribution. F. Comparing 6mA calling results for the Revio system, generated by the Sequel <t>kmer</t> model, human native gDNA control, and the Revio kmer model. FPR (grey shade) and FNR (red shade) were calculated after Gaussian demixing of IPDr distribution.
Pretrained Models Galactica, supplied by Galactica Pharma, 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/pretrained models galactica/product/Galactica Pharma
Average 90 stars, based on 1 article reviews
pretrained models galactica - by Bioz Stars, 2026-04
90/100 stars
  Buy from Supplier

Image Search Results


Overview of the Attention-Guided Erasing (AGE) Methodology. a Self-Supervised Pretraining using DINO [11]: A teacher student ViT framework, leveraging a teacher-student ViT-S self-distillation framework. b AGE [13]: Attention head visualizations from the SSL pretrained teacher ViT-S are converted into binary masks to isolate key ROIs and then used to erase background regions. c Transfer Learning with AGE: AGE is used on the input images using each of the attention heads with a random probability during training. The attention head yielding the highest validation performance is selected for final AGE-based transfer learning

Journal: International Journal of Computer Assisted Radiology and Surgery

Article Title: Attention-guided erasing for enhanced transfer learning in breast abnormality classification

doi: 10.1007/s11548-024-03317-6

Figure Lengend Snippet: Overview of the Attention-Guided Erasing (AGE) Methodology. a Self-Supervised Pretraining using DINO [11]: A teacher student ViT framework, leveraging a teacher-student ViT-S self-distillation framework. b AGE [13]: Attention head visualizations from the SSL pretrained teacher ViT-S are converted into binary masks to isolate key ROIs and then used to erase background regions. c Transfer Learning with AGE: AGE is used on the input images using each of the attention heads with a random probability during training. The attention head yielding the highest validation performance is selected for final AGE-based transfer learning

Article Snippet: Input image followed by six attention maps from each of the five pretrained DINO models associated with specific tasks: T1 (Breast Density in DM), T2 (Malignancy in CEM), T3 (Calcification ROI in DM), T4 (Malignancy ROI in CEM), and T5 (Mass ROI in DM).

Techniques: Distillation, Biomarker Discovery

Attention Head Visualizations. Input image followed by six attention maps from each of the five pretrained DINO models associated with specific tasks: T1 (Breast Density in DM), T2 (Malignancy in CEM), T3 (Calcification ROI in DM), T4 (Malignancy ROI in CEM), and T5 (Mass ROI in DM). The final selected attention heads used for transfer learning are highlighted in red

Journal: International Journal of Computer Assisted Radiology and Surgery

Article Title: Attention-guided erasing for enhanced transfer learning in breast abnormality classification

doi: 10.1007/s11548-024-03317-6

Figure Lengend Snippet: Attention Head Visualizations. Input image followed by six attention maps from each of the five pretrained DINO models associated with specific tasks: T1 (Breast Density in DM), T2 (Malignancy in CEM), T3 (Calcification ROI in DM), T4 (Malignancy ROI in CEM), and T5 (Mass ROI in DM). The final selected attention heads used for transfer learning are highlighted in red

Article Snippet: Input image followed by six attention maps from each of the five pretrained DINO models associated with specific tasks: T1 (Breast Density in DM), T2 (Malignancy in CEM), T3 (Calcification ROI in DM), T4 (Malignancy ROI in CEM), and T5 (Mass ROI in DM).

Techniques:

A. Schematic for SMRT CCS and 6mA calling. B. Comparing ipdSummary and ipdTrimming pipelines for 6mA calling using the subread level Sequel data. C. Performance of ipdSummary and ipdTrimming on four different datasets as indicated on left. The thresholds for calling 6mA are indicated by black lines. For dam + plasmid DNA dataset, the thresholds for 98.5% 6mA recall are indicated by red lines. The same recall thresholds are used to calculate the background noise level for 6mA calling in the WGA dataset. False positive rates (FPR, grey shade) and false negative rates (FNR, red shade) for both Tetrahymena gDNA and human 6mA-FP samples were calculated after Gaussian demixing of IPDr distribution. D. Comparing the computational costs, including job all clock time, CPU running time and peak memory usage of ipdSummary and ipdTrimming. E. Comparing 6mA calling results by ipdTrimming and fibertools. Red curves represent IPDr distribution for all adenine sites calculated by ipdTrimming; blue curves represent the number of adenine sites with indicated IPDr values that are called as 6mA by fibertools. The thresholds for 6mA calling by ipdTrimming are indicated by black lines. Recall rates (black label) and FPR (red label) were calculated after Gaussian demixing of IPDr distribution. F. Comparing 6mA calling results for the Revio system, generated by the Sequel kmer model, human native gDNA control, and the Revio kmer model. FPR (grey shade) and FNR (red shade) were calculated after Gaussian demixing of IPDr distribution.

Journal: bioRxiv

Article Title: Revealing long-range heterogeneous organization of nucleoproteins with N 6 -methyladenine footprinting

doi: 10.1101/2024.12.05.627052

Figure Lengend Snippet: A. Schematic for SMRT CCS and 6mA calling. B. Comparing ipdSummary and ipdTrimming pipelines for 6mA calling using the subread level Sequel data. C. Performance of ipdSummary and ipdTrimming on four different datasets as indicated on left. The thresholds for calling 6mA are indicated by black lines. For dam + plasmid DNA dataset, the thresholds for 98.5% 6mA recall are indicated by red lines. The same recall thresholds are used to calculate the background noise level for 6mA calling in the WGA dataset. False positive rates (FPR, grey shade) and false negative rates (FNR, red shade) for both Tetrahymena gDNA and human 6mA-FP samples were calculated after Gaussian demixing of IPDr distribution. D. Comparing the computational costs, including job all clock time, CPU running time and peak memory usage of ipdSummary and ipdTrimming. E. Comparing 6mA calling results by ipdTrimming and fibertools. Red curves represent IPDr distribution for all adenine sites calculated by ipdTrimming; blue curves represent the number of adenine sites with indicated IPDr values that are called as 6mA by fibertools. The thresholds for 6mA calling by ipdTrimming are indicated by black lines. Recall rates (black label) and FPR (red label) were calculated after Gaussian demixing of IPDr distribution. F. Comparing 6mA calling results for the Revio system, generated by the Sequel kmer model, human native gDNA control, and the Revio kmer model. FPR (grey shade) and FNR (red shade) were calculated after Gaussian demixing of IPDr distribution.

Article Snippet: The CCS IPD value was subsequently compared to a reference IPD value of its unmodified counterpart embedded in the same local sequence; all reference IPD values were generated by a pretrained kmer model ( https://github.com/PacificBiosciences/kineticsTools/tree/master/kineticsTools/resources ).

Techniques: Plasmid Preparation, Generated, Control