108 citations · 113 across the 7 of their papers we have counts for
8 papers
Compressed Active Subspaces for Scalable Bayesian Inference
Thomas Flynn, Sanket Jantre, Byung-Jun Yoon +1
Active subspace methods provide a framework for quantifying predictive uncertainty in high-dimensional models by identifying and performing inference along parameter directions tha…
Pathway-Guided Optimization of Deep Generative Molecular Design Models for Cancer Therapy
Alif Bin Abdul Qayyum, Susan D. Mertins, Amanda K. Paulson +2
The data-driven drug design problem can be formulated as an optimization task of a potentially expensive black-box objective function over a huge high-dimensional and structured mo…
Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders
A N M Nafiz Abeer, Sanket Jantre, Nathan M Urban +1
In recent years, deep generative models have been successfully adopted for various molecular design tasks, particularly in the life and material sciences. A critical challenge for…
Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Design
A N M Nafiz Abeer, Sanket Jantre, Nathan M Urban +1
Deep generative models have been accelerating the inverse design process in material and drug design. Unlike their counterpart property predictors in typical molecular design frame…
Identifying Bayesian Optimal Experiments for Uncertain Biochemical Pathway Models
Natalie M. Isenberg, Susan D. Mertins, Byung-Jun Yoon +2
Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive therapeuti…
Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks
Sanket Jantre, Nathan M. Urban, Xiaoning Qian +1
Bayesian inference for neural networks, or Bayesian deep learning, has the potential to provide well-calibrated predictions with quantified uncertainty and robustness. However, the…
Current and future directions in network biology
Marinka Zitnik, Michelle M. Li, Aydin Wells +34
Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseas…
Sequential Bayesian Neural Subnetwork Ensembles
Sanket Jantre, Shrijita Bhattacharya, Nathan M. Urban +4
Deep ensembles have emerged as a powerful technique for improving predictive performance and enhancing model robustness across various applications by leveraging model diversity. H…