HARRISBURG, PA — A team of faculty-researchers from Harrisburg University of Science & Technology (HU), comprised of Ziyuan Huang, Ph.D.; Roozbeh Sadeghian, Ph.D.; and Maria Vaida, Ph.D., have published a novel research paper exploring the applications of artificial intelligence (AI) in the early detection and classification of Alzheimer’s disease.
“ADAM-1: An AI Reasoning and Bioinformatics Model for Alzheimer’s Disease Detection and Microbiome-Clinical Data Integration” is newly published in Volume 3 of IEEE Xplore. The team also presented their findings at the 2025 Alzheimer’s Association International (AAIC) Conference in Toronto, Canada.
“Although we were not able to attend the conference in person this year, we were grateful for the opportunity to present our work virtually,” said Dr. Huang. “This project demonstrates close collaboration between UMass Chan Medical School, Harrisburg University, and Johns Hopkins University. Over the past two years, our interdisciplinary team has worked to integrate microbiome science, bioinformatics, and AI to develop new insights into Alzheimer’s disease.”
At AAIC 2025, the team introduced ADAM-1: a research-focused AI framework that integrates gut microbiome profiles, clinical variables, and biomedical literature to support Alzheimer’s disease classification. By combining statistical modeling, machine learning, large language models (LLM), and semantic search, the system improves both accuracy and interpretability – and essential step for high-dimensional, small-sample datasets often seen in neurodegenerative disease studies.
“The ADAM-1 framework is designed for biological investigation and hypothesis generation,” noted Dr. Huang. “One of its key features is the ability to trace predictions back to relevant scientific literature, allowing researchers to contextualize the model’s outputs better. We hope this work contributes to the development of more transparent and data-driven tools for studying complex diseases like Alzheimer’s.”
He continued: “We are especially grateful for the full collaboration from Harrisburg University faculty members Dr. Roozbeh Sadeghian and Dr. Maria L. Vaida, who provided critical insights into the AI components of the system.”
You can read the full paper at this link.
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