5 papers
Transformer-Based Active Learning for Data-Efficient Vaccine Epitope Selection in PRRS
Aspen Erlandsson Brisebois, Zahed Khatooni, Connor Burbridge +5
High-fidelity molecular docking simulations can produce biologically relevant estimates of epitope-receptor binding affinity but are computationally expensive and therefore limit t…
Exploring the Effects of Entanglement on Quantum Machine Learning of Pathogen Epitope-Receptor Binding
Aspen Erlandsson Brisebois, Luis Pablo Gonzalez Dominguez, Shivansi Prajapati +8
Parameterized quantum circuits (PQCs) provide a flexible substrate for hybrid quantum machine learning (QML), but their practical value on Noisy Intermediate-Scale Quantum (NISQ) d…
Identifying Protein Co-regulatory Network Logic by Solving B-SAT Problems through Gate-based Quantum Computing
Aspen Erlandsson Brisebois, Jason Broderick, Zahed Khatooni +3
There is growing awareness that the success of pharmacologic interventions on living organisms is significantly impacted by context and timing of exposure. In turn, this complexity…
Reconstructing Biological Pathways by Applying Selective Incremental Learning to (Very) Small Language Models
Pranta Saha, Joyce Reimer, Brook Byrns +5
The use of generative artificial intelligence (AI) models is becoming ubiquitous in many fields. Though progress continues to be made, general purpose large language AI models (LLM…
GPU-accelerated Modeling of Biological Regulatory Networks
Joyce Reimer, Pranta Saha, Chris Chen +4
The complex regulatory dynamics of a biological network can be succinctly captured using discrete logic models. Given even sparse time-course data from the system of interest, prev…