deep learning (dl) methods Search Results


deep learning (dl) algorithm based on 2d u-net (myomics-t1 software, version 1.0.0)  (Myomics Inc)
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Myomics Inc deep learning (dl) algorithm based on 2d u-net (myomics-t1 software, version 1.0.0)
Deep Learning (Dl) Algorithm Based On 2d U Net (Myomics T1 Software, Version 1.0.0), supplied by Myomics Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep-learning methods  (Chakshu Research Inc)
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Chakshu Research Inc deep-learning methods
The hyper-parameters describing the architecture of the MLP classifiers that produce the highest \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document} F 1 scores on the validation set with early stopping criterion for <t> AAA classification, </t> when using the best performing combinations of three to six input measurements
Deep Learning Methods, supplied by Chakshu Research Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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mammography-based deep learning (dl) model  (Mirai INC)
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Mirai INC mammography-based deep learning (dl) model
The hyper-parameters describing the architecture of the MLP classifiers that produce the highest \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document} F 1 scores on the validation set with early stopping criterion for <t> AAA classification, </t> when using the best performing combinations of three to six input measurements
Mammography Based Deep Learning (Dl) Model, supplied by Mirai INC, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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4d cbct image reconstruction method synergizing analytical method, iterative method, and deep learning  (AirNet Systems Inc)
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AirNet Systems Inc 4d cbct image reconstruction method synergizing analytical method, iterative method, and deep learning
The hyper-parameters describing the architecture of the MLP classifiers that produce the highest \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document} F 1 scores on the validation set with early stopping criterion for <t> AAA classification, </t> when using the best performing combinations of three to six input measurements
4d Cbct Image Reconstruction Method Synergizing Analytical Method, Iterative Method, And Deep Learning, supplied by AirNet Systems Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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apparatus and method using deep learning (dl) to improve analytical tomographic image reconstruction  (Canon inc)
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Canon inc apparatus and method using deep learning (dl) to improve analytical tomographic image reconstruction
The hyper-parameters describing the architecture of the MLP classifiers that produce the highest \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document} F 1 scores on the validation set with early stopping criterion for <t> AAA classification, </t> when using the best performing combinations of three to six input measurements
Apparatus And Method Using Deep Learning (Dl) To Improve Analytical Tomographic Image Reconstruction, supplied by Canon inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep learning-based methods  (Oxford Nanopore)
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Oxford Nanopore deep learning-based methods
The hyper-parameters describing the architecture of the MLP classifiers that produce the highest \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document} F 1 scores on the validation set with early stopping criterion for <t> AAA classification, </t> when using the best performing combinations of three to six input measurements
Deep Learning Based Methods, 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
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and deep learning (dl) models  (STMicroelectronics Pte)
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STMicroelectronics Pte and deep learning (dl) models
The hyper-parameters describing the architecture of the MLP classifiers that produce the highest \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document} F 1 scores on the validation set with early stopping criterion for <t> AAA classification, </t> when using the best performing combinations of three to six input measurements
And Deep Learning (Dl) Models, supplied by STMicroelectronics Pte, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep learning (dl) algorithms  (Barents Group LLC)
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Barents Group LLC deep learning (dl) algorithms
The hyper-parameters describing the architecture of the MLP classifiers that produce the highest \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document} F 1 scores on the validation set with early stopping criterion for <t> AAA classification, </t> when using the best performing combinations of three to six input measurements
Deep Learning (Dl) Algorithms, supplied by Barents Group LLC, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep (machine) learning base-calling methods  (Oxford Nanopore)
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Oxford Nanopore deep (machine) learning base-calling methods
The hyper-parameters describing the architecture of the MLP classifiers that produce the highest \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document} F 1 scores on the validation set with early stopping criterion for <t> AAA classification, </t> when using the best performing combinations of three to six input measurements
Deep (Machine) Learning Base Calling Methods, 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
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huanglongbing detection method for orange trees based on deep neural networks and transfer learning  (IEEE Access)
90
IEEE Access huanglongbing detection method for orange trees based on deep neural networks and transfer learning
The hyper-parameters describing the architecture of the MLP classifiers that produce the highest \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document} F 1 scores on the validation set with early stopping criterion for <t> AAA classification, </t> when using the best performing combinations of three to six input measurements
Huanglongbing Detection Method For Orange Trees Based On Deep Neural Networks And Transfer Learning, supplied by IEEE Access, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep learning (dl) interpretation  (Cureline Inc)
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Cureline Inc deep learning (dl) interpretation
The hyper-parameters describing the architecture of the MLP classifiers that produce the highest \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document} F 1 scores on the validation set with early stopping criterion for <t> AAA classification, </t> when using the best performing combinations of three to six input measurements
Deep Learning (Dl) Interpretation, supplied by Cureline Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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deep reinforcement learning based molecular de novo design method  (AstraZeneca ltd)
90
AstraZeneca ltd deep reinforcement learning based molecular de novo design method
( A ) The identification of ten commercial-accessible aromatic fragments aided by deep <t>reinforcement</t> learning model; ( B ) Synthesis of LFS-1107 via the installation of aromatic tetrazole moiety selected from the previous step to the sulforaphene parent structure; ( C ) Assessment of protein-ligand binding kinetics and binding affinity of tetrazole aromatic fragments via Bio-layer interferometry (BLI) assay; ( D ) Binding affinity of LFS-1107 and KPT-330 determined via BLI assay: LFS-1107, K d ~1.25E-11 M; KPT-330: K d ~5.29E-09 M. Figure 1—source data 1. The chemical structure of 10 commercial-accessible aromatic fragments. Figure 1—source data 2. The synthesis of compound LFS-1107. Figure 1—source data 3. The data of affinities and binding kinetics of CRM1 to S5 and S8. Figure 1—source data 4. The data of affinities and binding kinetics of CRM1 to LFS-1107 and KPT-330.
Deep Reinforcement Learning Based Molecular De Novo Design Method, supplied by AstraZeneca ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Image Search Results


The hyper-parameters describing the architecture of the MLP classifiers that produce the highest \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document} F 1 scores on the validation set with early stopping criterion for AAA classification, when using the best performing combinations of three to six input measurements

Journal: Biomechanics and Modeling in Mechanobiology

Article Title: Machine learning for detection of stenoses and aneurysms: application in a physiologically realistic virtual patient database

doi: 10.1007/s10237-021-01497-7

Figure Lengend Snippet: The hyper-parameters describing the architecture of the MLP classifiers that produce the highest \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$F_1$$\end{document} F 1 scores on the validation set with early stopping criterion for AAA classification, when using the best performing combinations of three to six input measurements

Article Snippet: A previous study (Chakshu et al. ) has applied deep-learning methods to AAA classification, using a synthetic data set created by varying seven parameters.

Techniques: Biomarker Discovery

( A ) The identification of ten commercial-accessible aromatic fragments aided by deep reinforcement learning model; ( B ) Synthesis of LFS-1107 via the installation of aromatic tetrazole moiety selected from the previous step to the sulforaphene parent structure; ( C ) Assessment of protein-ligand binding kinetics and binding affinity of tetrazole aromatic fragments via Bio-layer interferometry (BLI) assay; ( D ) Binding affinity of LFS-1107 and KPT-330 determined via BLI assay: LFS-1107, K d ~1.25E-11 M; KPT-330: K d ~5.29E-09 M. Figure 1—source data 1. The chemical structure of 10 commercial-accessible aromatic fragments. Figure 1—source data 2. The synthesis of compound LFS-1107. Figure 1—source data 3. The data of affinities and binding kinetics of CRM1 to S5 and S8. Figure 1—source data 4. The data of affinities and binding kinetics of CRM1 to LFS-1107 and KPT-330.

Journal: eLife

Article Title: Discovery and biological evaluation of a potent small molecule CRM1 inhibitor for its selective ablation of extranodal NK/T cell lymphoma

doi: 10.7554/eLife.80625

Figure Lengend Snippet: ( A ) The identification of ten commercial-accessible aromatic fragments aided by deep reinforcement learning model; ( B ) Synthesis of LFS-1107 via the installation of aromatic tetrazole moiety selected from the previous step to the sulforaphene parent structure; ( C ) Assessment of protein-ligand binding kinetics and binding affinity of tetrazole aromatic fragments via Bio-layer interferometry (BLI) assay; ( D ) Binding affinity of LFS-1107 and KPT-330 determined via BLI assay: LFS-1107, K d ~1.25E-11 M; KPT-330: K d ~5.29E-09 M. Figure 1—source data 1. The chemical structure of 10 commercial-accessible aromatic fragments. Figure 1—source data 2. The synthesis of compound LFS-1107. Figure 1—source data 3. The data of affinities and binding kinetics of CRM1 to S5 and S8. Figure 1—source data 4. The data of affinities and binding kinetics of CRM1 to LFS-1107 and KPT-330.

Article Snippet: In the present study, we adopted the deep reinforcement learning based molecular de novo design method developed by Olivecrona etc. from AstraZeneca.

Techniques: Ligand Binding Assay, Binding Assay