Data Scientist Yi Nian Documents Machine Learning Role in Risk Prediction of Alzheimer’s and Related Dementias
Data Scientist Yi Nian has advanced Alzheimer’s research at the University of Texas Health Science Center, highlighting machine learning’s role in accurate ADRD risk prediction. His work addresses the sixth leading U.S. cause of death, uncovering key risk factors to improve prevention and understanding.
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SEATTLE, Wash. (December 3, 2024) – Renowned Data Scientist Yi Nian has made a significant contribution to the field of Alzheimer’s Disease research through his work at the University of Texas Health Science Center and resulting scholarly articles. With Alzheimer’s Disease and related dementias (ADRD) ranking as the sixth leading cause of death in the United States, Nian’s work aims to underscore the importance of accurate ADRD risk prediction and the critical role of machine learning in revealing additional risk factors.
With no effective treatments to date for most neurodegenerative diseases, Nian’s documentations offer a deep dive into the role of knowledge graphs as comprehensive and semantic representations that have been successfully leveraged in biomedical applications including drug repurposing. His objective is to construct a knowledge graph from literature to study the relations between Alzheimer’s and chemicals, drugs and dietary supplements in order to identify opportunities to prevent or delay neurodegenerative progression based on risk prediction.
“They key to Alzheimer’s and related dementia prevention or delay is understanding risk factors early,” said Nian. “My work uses variationally regularized encoder-decoder graph neural networks (GNNs) integrated with a proposed relation importance method for estimating ADRD likelihood. This self-explainable method can provide a feature-important explanation in the context of ADRD risk prediction, leveraging relational information within a graph. Three scenarios with 1-year, 2-year, and 3-year prediction windows were created to assess the model’s efficiency, respectively. Random forest (RF) and light gradient boost machine (LGBM) were used as baselines. By using this method, we further clarify the key relationships for ADRD risk prediction.”
Nian’s ADRD research is documented in a series of scholarly articles, including Mining On Alzheimer’s Diseases Related Knowledge Graph to Identity Potential AD-related Semantic Triples for Drug Repurposing, Self-explainable graph neural network for Alzheimer disease and related dementias risk prediction: Algorithm development and validation study, Explainable Graph Neural Network for Alzheimer’s Disease And Related Dementias Risk Prediction, Knowledge Graph-based Neurodegenerative Diseases and Diet Relationship Discovery, and more.
From 2021-2022, Nian was a Data Science Research Assistant for UTHealth Science Center in Houston, Texas, where he constructed knowledge graphs with NLP tools from Alzheimer’s Disease literature. He conducted research in state-of-the-art graph neural network methods for biomedical knowledge graphs and designed innovative graph mining methods for drug repurposing and drug discovery. Nian now works as an Applied Scientist with Amazon, specifically focused on the AWS AI Lab and Prime Video, where he created a science-driven solution for a multilingual abstractive summarization function using large language models (LLM) and collaborated with the SDE team to integrate this feature into AWS Transcribe. To date, Nian has authored 16 scholarly articles based on his machine-learning and GNN expertise, which have been cited 164 times in related research.
Nian holds a Master’s degree in computer science from the University of Chicago, a Master’s degree in statistics from Columbia University, and a Bachelor’s degree in mathematics from Ohio State University.
The full list of Nian’s research articles, including those related to his Alzheimer’s Disease and related dementias research, can be found online here.
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Contact Info:
Name: Yi Nian
Email: Send Email
Organization: Yi Nian
Website: https://scholar.google.com/citations?view_op=list_works&hl=en&hl=en&user=RBTCmEkAAAAJ
Release ID: 89147802
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