2 citations · 3 across the 5 of their papers we have counts for
6 papers
MarkerScout: A Disease-Agnostic Machine Learning Framework for Biomarker Prediction from Multi-Scale Mechanistic Models
Robert Moore, Frank Agayie-Ntim, Lindsey B. Crawford +5
We demonstrate the framework on three infectious diseases derived from a companion mechanistic immune-simulation platform: SARS-CoV-2, Influenza A Virus, and Plasmodium falciparum.…
MechAInistic: An LLM-guided Multi-Agent System for Reasoning over Genome-Scale Constraint-Based Metabolic Models
Josh Loecker, Narayna Puraja, William Bryan +3
Constraint-based metabolic modeling is a powerful way to study the mechanistic basis of cellular states and disease, but its effective use demands substantial computational experti…
From Knowledge Generation to Knowledge Verification: Examining the BioMedical Generative Capabilities of ChatGPT
Ahmed Abdeen Hamed, Alessandro Crimi, Magdalena M. Misiak +1
The generative capabilities of LLM models offer opportunities for accelerating tasks but raise concerns about the authenticity of the knowledge they produce. To address these conce…
Reinforcement of Explainability of ChatGPT Prompts by Embedding Breast Cancer Self-Screening Rules into AI Responses
Yousef Khan, Ahmed Abdeen Hamed
Addressing the global challenge of breast cancer, this research explores the fusion of generative AI, focusing on ChatGPT 3.5 turbo model, and the intricacies of breast cancer risk…
Quantifying Similarity: Text-Mining Approaches to Evaluate ChatGPT and Google Bard Content in Relation to BioMedical Literature
Jakub Klimczak, Ahmed Abdeen Hamed
Background: The emergence of generative AI tools, empowered by Large Language Models (LLMs), has shown powerful capabilities in generating content. To date, the assessment of the u…
Fact-Checking Generative AI: Ontology-Driven Biological Graphs for Disease-Gene Link Verification
Ahmed Abdeen Hamed, Byung Suk Lee, Alessandro Crimi +1
Since the launch of various generative AI tools, scientists have been striving to evaluate their capabilities and contents, in the hope of establishing trust in their generative ab…