3 citations · 10 across the 19 of their papers we have counts for
19 papers
Knowledge Acquisition During Pre-training? Large Language Models Learn Better With Auxiliary Views
Joseph Lee, Yidi Huang, Dokyoon Kim +2
Gaps remain in our understanding of how large language models (LLMs) acquire knowledge during pre-training. We find that auxiliary views, conceptual reformulations, are key to this…
iLENS: Interpretable LLM-Guided Mixture-of-Experts for Neuroimaging Survival Analysis
Farica Zhuang, Seong Woo Han, Zixuan Wen +3
Alzheimer's Disease (AD) is a complex neurodegenerative disorder that continues to impact millions of people worldwide. Predicting AD conversion during the prodromal stage remains…
A Semantic-Sampling Framework for Evaluating Calibration in Open-Ended Question Answering
Zhanliang Wang, Jiancong Xiao, Ruochen Jin +3
Calibration measures whether a model's predicted confidence aligns with its empirical accuracy, and is central to the reliable deployment of large language models (LLMs) in high-st…
Tabular LLMs for Interpretable Few-Shot Alzheimer's Disease Prediction with Multimodal Biomedical Data
Sophie Kearney, Shu Yang, Zixuan Wen +8
Accurate diagnosis of Alzheimer's disease (AD) requires handling tabular biomarker data, yet such data are often small and incomplete, where deep learning models frequently fail to…
Interpretable Alzheimer's Diagnosis via Multimodal Fusion of Regional Brain Experts
Farica Zhuang, Shu Yang, Dinara Aliyeva +6
Accurate and early diagnosis of Alzheimer's disease (AD) is critical for effective intervention and requires integrating complementary information from multimodal neuroimaging data…
Meta-Router: Bridging Gold-standard and Preference-based Evaluations in Large Language Model Routing
Yichi Zhang, Fangzheng Xie, Shu Yang +1
In language tasks that require extensive human--model interaction, deploying a single "best" model for every query can be expensive. To reduce inference cost while preserving the q…
Enabling Few-Shot Alzheimer's Disease Diagnosis on Biomarker Data with Tabular LLMs
Sophie Kearney, Shu Yang, Zixuan Wen +6
Early and accurate diagnosis of Alzheimer's disease (AD), a complex neurodegenerative disorder, requires analysis of heterogeneous biomarkers (e.g., neuroimaging, genetic risk fact…
Penalized FCI for Causal Structure Learning in a Sparse DAG for Biomarker Discovery in Parkinson's Disease
Samhita Pal, Dhrubajyoti Ghosh, Shu Yang
Parkinson's disease (PD) is a progressive neurodegenerative disorder that lacks reliable early-stage biomarkers for diagnosis, prognosis, and therapeutic monitoring. While cerebros…
ICAFS: Inter-Client-Aware Feature Selection for Vertical Federated Learning
Ruochen Jin, Boning Tong, Shu Yang +2
Vertical federated learning (VFL) enables a paradigm for vertically partitioned data across clients to collaboratively train machine learning models. Feature selection (FS) plays a…
Crowdsourcing-Based Knowledge Graph Construction for Drug Side Effects Using Large Language Models with an Application on Semaglutide
Zhijie Duan, Kai Wei, Zhaoqian Xue +5
Social media is a rich source of real-world data that captures valuable patient experience information for pharmacovigilance. However, mining data from unstructured and noisy socia…
From Promising Capability to Pervasive Bias: Assessing Large Language Models for Emergency Department Triage
Joseph Lee, Tianqi Shang, Jae Young Baik +4
Large Language Models (LLMs) have shown promise in clinical decision support, yet their application to triage remains underexplored. We systematically investigate the capabilities…
MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance
Jia Xu, Tianyi Wei, Bojian Hou +7
We introduce MentalChat16K, an English benchmark dataset combining a synthetic mental health counseling dataset and a dataset of anonymized transcripts from interventions between B…
Accessing the topological properties of human brain functional sub-circuits in Echo State Networks
Bach Nguyen, Tianlong Chen, Shu Yang +3
Recent years have witnessed an emerging trend in neuromorphic computing that centers around the use of brain connectomics as a blueprint for artificial neural networks. Connectomic…
Advances in RNA secondary structure prediction and RNA modifications: Methods, data, and applications
Shu Yang, Nhat Truong Pham, Ziyang Li +10
Due to the hierarchical organization of RNA structures and their pivotal roles in fulfilling RNA functions, the formation of RNA secondary structure critically influences many biol…
Clustering Alzheimer's Disease Subtypes via Similarity Learning and Graph Diffusion
Tianyi Wei, Shu Yang, Davoud Ataee Tarzanagh +6
Alzheimer's disease (AD) is a complex neurodegenerative disorder that affects millions of people worldwide. Due to the heterogeneous nature of AD, its diagnosis and treatment pose…
Leveraging Social Determinants of Health in Alzheimer's Research Using LLM-Augmented Literature Mining and Knowledge Graphs
Tianqi Shang, Shu Yang, Weiqing He +7
Growing evidence suggests that social determinants of health (SDoH), a set of nonmedical factors, affect individuals' risks of developing Alzheimer's disease (AD) and related demen…
Knowledge-Driven Feature Selection and Engineering for Genotype Data with Large Language Models
Joseph Lee, Shu Yang, Jae Young Baik +8
Predicting phenotypes with complex genetic bases based on a small, interpretable set of variant features remains a challenging task. Conventionally, data-driven approaches are util…
Semiparametric Localized Principal Stratification Analysis with Continuous Strata
Yichi Zhang, Shu Yang
Principal stratification is essential for revealing causal mechanisms involving post-treatment intermediate variables, in real-world applications like surrogate marker evaluation.…
DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literature
Dawei Li, Shu Yang, Zhen Tan +10
Recent advancements in large language models (LLMs) have achieved promising performances across various applications. Nonetheless, the ongoing challenge of integrating long-tail kn…