3 citations · 10 across the 19 of their papers we have counts for
22 papers
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…
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…
Alpha-Z divergence unveils further distinct phenotypic traits of human brain connectivity fingerprint
Md Kaosar Uddin, Nghi Nguyen, Huajun Huang +2
The accurate identification of individuals from functional connectomes (FCs) is critical for advancing individualized assessments in neuropsychiatric research. Traditional methods,…
Fair CCA for Fair Representation Learning: An ADNI Study
Bojian Hou, Zhanliang Wang, Zhuoping Zhou +6
Canonical correlation analysis (CCA) is a technique for finding correlations between different data modalities and learning low-dimensional representations. As fairness becomes cru…
Reconstructing Brain Causal Dynamics for Subject and Task Fingerprints using fMRI Time-series Data
Dachuan Song, Li Shen, Duy Duong-Tran +1
Purpose: Recently, there has been a revived interest in system neuroscience causation models, driven by their unique capability to unravel complex relationships in multi-scale brai…
Data-Driven Stabilization of Unknown Linear-Threshold Network Dynamics
Xuan Wang, Duy Duong-Tran, Jorge Cortés
This paper studies the data-driven control of unknown linear-threshold network dynamics to stabilize the state to a reference value. We consider two types of controllers: (i) a sta…
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…
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…
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…
Causality-based Subject and Task Fingerprints using fMRI Time-series Data
Dachuan Song, Li Shen, Duy Duong-Tran +1
Recently, there has been a revived interest in system neuroscience causation models due to their unique capability to unravel complex relationships in multi-scale brain networks. I…
Volume-optimal persistence homological scaffolds of hemodynamic networks covary with MEG theta-alpha aperiodic dynamics
Nghi Nguyen, Tao Hou, Enrico Amico +9
Higher-order properties of functional magnetic resonance imaging (fMRI) induced connectivity have been shown to unravel many exclusive topological and dynamical insights beyond pai…
A principled framework to assess the information-theoretic fitness of brain functional sub-circuits
Duy Duong-Tran, Nghi Nguyen, Shizhuo Mu +10
In systems and network neuroscience, many common practices in brain connectomic analysis are often not properly scrutinized. One such practice is mapping a predetermined set of sub…
Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model
Duy M. H. Nguyen, An T. Le, Trung Q. Nguyen +7
Prompt learning methods are gaining increasing attention due to their ability to customize large vision-language models to new domains using pre-trained contextual knowledge and mi…
Theorizing neuro-induced relationships between cognitive diversity, motivation, grit and academic performance in multidisciplinary engineering education context
Duy Duong-Tran, Siqing Wei, Li Shen
Nowadays, engineers need to tackle many unprecedented challenges that are often complex, and, most importantly, cannot be exhaustively compartmentalized into a single engineering d…
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…
Tangent functional connectomes uncover more unique phenotypic traits
Kausar Abbas, Mintao Liu, Michael Wang +9
Functional connectomes (FCs) contain pairwise estimations of functional couplings based on pairs of brain regions activity. FCs are commonly represented as correlation matrices tha…
Improving Functional Connectome Fingerprinting with Degree-Normalization
Benjamin Chiêm, Kausar Abbas, Enrico Amico +3
Functional connectivity quantifies the statistical dependencies between the activity of brain regions, measured using neuroimaging data such as functional MRI BOLD time series. The…
GEFF: Graph Embedding for Functional Fingerprinting
Kausar Abbas, Enrico Amico, Diana Otero Svaldi +7
It has been well established that Functional Connectomes (FCs), as estimated from functional MRI (fMRI) data, have an individual fingerprint that can be used to identify an individ…
Towards an information theoretical description of communication in brain networks
Enrico Amico, Kausar Abbas, Duy Anh Duong-Tran +6
Modeling communication dynamics in the brain is a key challenge in network neuroscience. We present here a framework that combines two measurements for any system where different c…
A morphospace of functional configuration to assess configural breadth based on brain functional networks
Duy Duong-Tran, Kausar Abbas, Enrico Amico +5
The best approach to quantify human brain functional reconfigurations in response to varying cognitive demands remains an unresolved topic in network neuroscience. We propose that…