most citedInferred global dense residue transition graphs from primary structure sequences enable protein interaction prediction via directed graph convolutional neural networks

1 citations · 3 across the 8 of their papers we have counts for

collaborators

10 papers

cs.LG2026

Arithmetic Simplicity in Stochastic Gradient Methods

Bin Fu, Pengfei Gu, Jose Nunez +1

A gradient descent method is arithmetically simple if the operations are limited to , and division with integer . An arthmetically simple gradient method is…

cs.LG2026

Longitudinal Bayesian Learning of Continuous Disease Position across the Alzheimer's Disease Continuum

Yingying Zhang, Kun Zhao, Guodong Liu +10

Alzheimer's disease (AD) progresses as a continuous biological process, whereas most existing neuroimaging-based artificial intelligence methods remain limited to discrete diagnosi…

cs.CV2026

Patch-MoE Mamba: A Patch-Ordered Mixture-of-Experts State Space Architecture for Medical Image Segmentation

Diego Adame, Fabian Vazquez, Jose A. Nunez +7

CNN- and Transformer-based architectures have achieved strong performance in medical image segmentation, but CNNs are limited in modeling long-range dependencies, while Transformer…

cs.CV2026★ 1 cited

Learning with Geometric Priors in U-Net Variants for Polyp Segmentation

Fabian Vazquez, Jose A. Nuñez, Diego Adame +7

Accurate and robust polyp segmentation is essential for early colorectal cancer detection and for computer-aided diagnosis. While convolutional neural network-, Transformer-, and M…

cs.LG2025★ 1 cited

R-GenIMA: Integrating Neuroimaging and Genetics with Interpretable Multimodal AI for Alzheimer's Disease Progression

Kun Zhao, Siyuan Dai, Yingying Zhang +9

Early detection of Alzheimer's disease (AD) requires models capable of integrating macro-scale neuroanatomical alterations with micro-scale genetic susceptibility, yet existing mul…

cs.CV2025

Integrating Multi-scale and Multi-filtration Topological Features for Medical Image Classification

Pengfei Gu, Huimin Li, Haoteng Tang +6

Modern deep neural networks have shown remarkable performance in medical image classification. However, such networks either emphasize pixel-intensity features instead of fundament…

q-bio.NC2025

Modeling Causal Interactions Across Brain Functional Subnetworks for Population-specific Disease Analysis

Alissen Moreno, Yingying Zhang, Qi Huang +9

Current neuroimaging studies on neurodegenerative diseases and psychological risk factors have been developed predominantly in non Hispanic White cohorts, with other populations ma…

cs.LG2025★ 1 cited

Inferred global dense residue transition graphs from primary structure sequences enable protein interaction prediction via directed graph convolutional neural networks

Islam Akef Ebeid, Haoteng Tang, Pengfei Gu

Introduction Accurate prediction of protein-protein interactions (PPIs) is crucial for understanding cellular functions and advancing drug development. Existing in-silico methods u…

cs.CV2025

Adapting a Segmentation Foundation Model for Medical Image Classification

Pengfei Gu, Haoteng Tang, Islam A. Ebeid +7

Recent advancements in foundation models, such as the Segment Anything Model (SAM), have shown strong performance in various vision tasks, particularly image segmentation, due to t…

eess.IV2025

Topo-VM-UNetV2: Encoding Topology into Vision Mamba UNet for Polyp Segmentation

Diego Adame, Jose A. Nunez, Fabian Vazquez +5

Convolutional neural network (CNN) and Transformer-based architectures are two dominant deep learning models for polyp segmentation. However, CNNs have limited capability for model…