31 citations · 64 across the 7 of their papers we have counts for
7 papers
Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge
Gilchan Park, Byung-Jun Yoon, Xihaier Luo +3
Background: Identification of the interactions and regulatory relations between biomolecules play pivotal roles in understanding complex biological systems and the mechanisms under…
Comprehensive analysis of gene expression profiles to radiation exposure reveals molecular signatures of low-dose radiation response
Xihaier Luo, Sean McCorkle, Gilchan Park +6
There are various sources of ionizing radiation exposure, where medical exposure for radiation therapy or diagnosis is the most common human-made source. Understanding how gene exp…
A Rigorous Uncertainty-Aware Quantification Framework Is Essential for Reproducible and Replicable Machine Learning Workflows
Line Pouchard, Kristofer G. Reyes, Francis J. Alexander +1
The ability to replicate predictions by machine learning (ML) or artificial intelligence (AI) models and results in scientific workflows that incorporate such ML/AI predictions is…
Multi-Objective Latent Space Optimization of Generative Molecular Design Models
A N M Nafiz Abeer, Nathan Urban, M Ryan Weil +2
Molecular design based on generative models, such as variational autoencoders (VAEs), has become increasingly popular in recent years due to its efficiency for exploring high-dimen…
Adaptive Group Testing with Mismatched Models
Mingzhou Fan, Byung-Jun Yoon, Francis J. Alexander +2
Accurate detection of infected individuals is one of the critical steps in stopping any pandemic. When the underlying infection rate of the disease is low, testing people in groups…
Robust Importance Sampling for Error Estimation in the Context of Optimal Bayesian Transfer Learning
Omar Maddouri, Xiaoning Qian, Francis J. Alexander +2
Classification has been a major task for building intelligent systems as it enables decision-making under uncertainty. Classifier design aims at building models from training data…
Optimal Decision Making in High-Throughput Virtual Screening Pipelines
Hyun-Myung Woo, Xiaoning Qian, Li Tan +4
The need for efficient computational screening of molecular candidates that possess desired properties frequently arises in various scientific and engineering problems, including d…