DDN2.0: R and Python packages for differential dependency network analysis of biological systems

Zhang, B; Fu, Yue; Lu, Yilong; Zhang, Z; Clarke, Robert; Je, Van Eyk; Dm, Herrington; Wang, Y

doi:10.1101/2021.04.10.439301

Cited by 2 publications

(2 citation statements)

References 35 publications

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“…As discussed above, the proposed EPGN (or NL-EPGN) consists of K phases, where each phase imitates one iteration in the accelerated extra gradient Algorithm 1 with proximal steps (6b) and (6e) replaced by (11) (or (12) for NL-EPGN). The flowchart of variables in the kth phase is shown in Figure 3.…”

Section: Network Trainingmentioning

confidence: 99%

“…Our goal in this paper is to propose an efficient extra proximal gradient algorithm that employs the Nesterov’s acceleration technique and the extra gradient scheme, and unroll this algorithm into a deep neural network called the extra proximal gradient network (EPGN) to solve a class of inverse problems ( 1 ). Motivated by the least absolute shrinkage and selection operator (LASSO) [ 11 , 12 , 13 ], our EPGN implicitly adopts an

-type regularization in ( 1 ) with a nonlinear sparsification mapping learned from data. The proximal operator of this regularization is elaborated by several linear convolutions, nonlinear activation functions, and shrinkage operations for robust sparse feature selection in EPGN.…”

Section: Introductionmentioning

confidence: 99%

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Extra Proximal-Gradient Network with Learned Regularization for Image Compressive Sensing Reconstruction

Zhang

Ye²,

Chen³

2022

J. Imaging

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Learned optimization algorithms are promising approaches to inverse problems by leveraging advanced numerical optimization schemes and deep neural network techniques in machine learning. In this paper, we propose a novel deep neural network architecture imitating an extra proximal gradient algorithm to solve a general class of inverse problems with a focus on applications in image reconstruction. The proposed network features learned regularization that incorporates adaptive sparsification mappings, robust shrinkage selections, and nonlocal operators to improve solution quality. Numerical results demonstrate the improved efficiency and accuracy of the proposed network over several state-of-the-art methods on a variety of test problems.

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Section: Network Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extra Proximal-Gradient Network with Learned Regularization for Image Compressive Sensing Reconstruction

Zhang

Ye²,

Chen³

2022

J. Imaging

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Uncertainty Quantification and Interpretability for Clinical Trial Approval Prediction

Lu,

Chen,

Hao

et al. 2024

Health Data Sci

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Background: Clinical trial is a crucial step in the development of a new therapy (e.g., medication) and is remarkably expensive and time-consuming. Forecasting the approval of clinical trials accurately would enable us to circumvent trials destined to fail, thereby allowing us to allocate more resources to therapies with better chances. However, existing approval prediction algorithms did not quantify the uncertainty and provide interpretability, limiting their usage in real-world clinical trial management. Methods: This paper quantifies uncertainty and improves interpretability in clinical trial approval predictions. We devised a selective classification approach and integrated it with the Hierarchical Interaction Network, the state-of-the-art clinical trial prediction model. Selective classification, encompassing a spectrum of methods for uncertainty quantification, empowers the model to withhold decision-making in the face of samples marked by ambiguity or low confidence. This approach not only amplifies the accuracy of predictions for the instances it chooses to classify but also notably enhances the model’s interpretability. Results: Comprehensive experiments demonstrate that incorporating uncertainty markedly enhances the model’s performance. Specifically, the proposed method achieved 32.37%, 21.43%, and 13.27% relative improvement on area under the precision–recall curve over the base model (Hierarchical Interaction Network) in phase I, II, and III trial approval predictions, respectively. For phase III trials, our method reaches 0.9022 area under the precision–recall curve scores. In addition, we show a case study of interpretability that helps domain experts to understand model’s outcome. The code is publicly available at https://github.com/Vincent-1125/Uncertainty-Quantification-on-Clinical-Trial-Outcome-Prediction . Conclusion: Our approach not only measures model uncertainty but also greatly improves interpretability and performance for clinical trial approval prediction.

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DDN2.0: R and Python packages for differential dependency network analysis of biological systems

Cited by 2 publications

References 35 publications

Extra Proximal-Gradient Network with Learned Regularization for Image Compressive Sensing Reconstruction

Extra Proximal-Gradient Network with Learned Regularization for Image Compressive Sensing Reconstruction

Uncertainty Quantification and Interpretability for Clinical Trial Approval Prediction

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