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09/12/2023
Identifying Bayesian Optimal Experiments for Uncertain Biochemical Pathway Models
Pharmacodynamic (PD) models are mathematical models of cellular reaction...
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09/06/2023
Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks
Bayesian inference for neural networks, or Bayesian deep learning, has t...
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07/17/2023
Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge
Understanding protein interactions and pathway knowledge is crucial for ...
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01/03/2023
Comprehensive analysis of gene expression profiles to radiation exposure reveals molecular signatures of low-dose radiation response
There are various sources of ionizing radiation exposure, where medical ...
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03/14/2022
Neural Message Passing for Objective-Based Uncertainty Quantification and Optimal Experimental Design
Real-world scientific or engineering applications often involve mathemat...
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03/01/2022
Multi-Objective Latent Space Optimization of Generative Molecular Design Models
Molecular design based on generative models, such as variational autoenc...
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10/05/2021
Adaptive Group Testing with Mismatched Models
Accurate detection of infected individuals is one of the critical steps ...
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09/23/2021
Optimal Decision Making in High-Throughput Virtual Screening Pipelines
Effective selection of the potential candidates that meet certain condit...
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09/05/2021
Robust Importance Sampling for Error Estimation in the Context of Optimal Bayesian Transfer Learning
Classification has been a major task for building intelligent systems as...
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03/26/2021
Geometric Affinity Propagation for Clustering with Network Knowledge
Clustering data into meaningful subsets is a major task in scientific da...
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10/07/2020
Quantifying the multi-objective cost of uncertainty
Various real-world applications involve modeling complex systems with im...
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07/12/2020