16 citations · 66 across the 38 of their papers we have counts for
38 papers
BioM-JEPA: joint-embedding prediction of graph-connected gene blocks in single cells
Yuhao Wang, Zelin Zang, Yuxuan Liu +2
Single-cell transcriptomes are sparse observations of coordinated biological programmes, yet most self-supervised models learn by reconstructing individual genes. Here we present B…
scHelix: Asymmetric Dual-Stream Integration via Explicit Gene-Level Disentanglement
Xichen Yan, Zelin Zang, Changxi Chi +8
A critical challenge in single-cell RNA sequencing (scRNA-seq) integration is resolving the tension between eliminating batch effects and maintaining biological fidelity. While rec…
MAT-Cell: A Multi-Agent Tree-Structured Reasoning Framework for Batch-Level Single-Cell Annotation
Yehui Yang, Zelin Zang, Xienan Zheng +9
Automated single-cell annotation is difficult when the most abundant genes are not the most discriminative ones, or when a target state is poorly covered by a fixed reference atlas…
Revisiting Cross-Attention Mechanisms: Leveraging Beneficial Noise for Domain-Adaptive Learning
Zelin Zang, Yehui Yang, Fei Wang +2
Unsupervised Domain Adaptation (UDA) seeks to transfer knowledge from a labeled source domain to an unlabeled target domain but often suffers from severe domain and scale gaps that…
SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos
Jinlin Wu, Felix Holm, Chuxi Chen +18
While foundation models have advanced surgical video analysis, current approaches rely predominantly on pixel-level reconstruction objectives that waste model capacity on low-level…
Anatomy-R1: Enhancing Anatomy Reasoning in Multimodal Large Language Models via Anatomical Similarity Curriculum and Group Diversity Augmentation
Ziyang Song, Zelin Zang, Zuyao Chen +6
Multimodal Large Language Models (MLLMs) have achieved impressive progress in natural image reasoning, yet their potential in medical imaging remains underexplored, especially in c…
NeuroABench: A Multimodal Evaluation Benchmark for Neurosurgical Anatomy Identification
Ziyang Song, Zelin Zang, Xiaofan Ye +7
Multimodal Large Language Models (MLLMs) have shown significant potential in surgical video understanding. With improved zero-shot performance and more effective human-machine inte…
Learning Cell-Aware Hierarchical Multi-Modal Representations for Robust Molecular Modeling
Mengran Li, Zelin Zang, Wenbin Xing +4
Understanding how chemical perturbations propagate through biological systems is essential for robust molecular property prediction. While most existing methods focus on chemical s…
Departures: Distributional Transport for Single-Cell Perturbation Prediction with Neural Schrödinger Bridges
Changxi Chi, Yufei Huang, Jun Xia +4
Predicting single-cell perturbation outcomes directly advances gene function analysis and facilitates drug candidate selection, making it a key driver of both basic and translation…
MedLA: A Logic-Driven Multi-Agent Framework for Complex Medical Reasoning with Large Language Models
Siqi Ma, Jiajie Huang, Fan Zhang +5
Answering complex medical questions requires not only domain expertise and patient-specific information, but also structured and multi-perspective reasoning. Existing multi-agent a…
HDTree: Generative Modeling of Cellular Hierarchies for Robust Lineage Inference
Zelin Zang, WenZhe Li, Yongjie Xu +5
In single-cell research, tracing and analyzing high-throughput single-cell differentiation trajectories is crucial for understanding biological processes. Key to this is the robust…
Doloris: Dual Conditional Diffusion Implicit Bridges with Sparsity Masking Strategy for Unpaired Single-Cell Perturbation Estimation
Changxi Chi, Jun Xia, Yufei Huang +9
Estimating single-cell responses across various perturbations facilitates the identification of key genes and enhances drug screening, significantly boosting experimental efficienc…
PCaM: A Progressive Focus Attention-Based Information Fusion Method for Improving Vision Transformer Domain Adaptation
Zelin Zang, Fei Wang, Liangyu Li +4
Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. Recent UDA methods based on Vision Transformers (ViTs) h…
PhyloGen: Language Model-Enhanced Phylogenetic Inference via Graph Structure Generation
ChenRui Duan, Zelin Zang, Siyuan Li +2
Phylogenetic trees elucidate evolutionary relationships among species, but phylogenetic inference remains challenging due to the complexity of combining continuous (branch lengths)…
MoE-Enhanced Explainable Deep Manifold Transformation for Complex Data Embedding and Visualization
Zelin Zang, Yuhao Wang, Jinlin Wu +4
Dimensionality reduction (DR) plays a crucial role in various fields, including data engineering and visualization, by simplifying complex datasets while retaining essential inform…
A Review of BioTree Construction in the Context of Information Fusion: Priors, Methods, Applications and Trends
Zelin Zang, Yongjie Xu, Chenrui Duan +4
Biological tree (BioTree) analysis is a foundational tool in biology, enabling the exploration of evolutionary and differentiation relationships among organisms, genes, and cells.…
GenBench: A Benchmarking Suite for Systematic Evaluation of Genomic Foundation Models
Zicheng Liu, Jiahui Li, Siyuan Li +5
The Genomic Foundation Model (GFM) paradigm is expected to facilitate the extraction of generalizable representations from massive genomic data, thereby enabling their application…
Set-CLIP: Exploring Aligned Semantic From Low-Alignment Multimodal Data Through A Distribution View
Zijia Song, Zelin Zang, Yelin Wang +5
Multimodal fusion breaks through the boundaries between diverse modalities and has already achieved notable performances. However, in many specialized fields, it is struggling to o…
Deep Multi-Manifold Transformation Based Multivariate Time Series Fault Detection
Hong Liu, Xiuxiu Qiu, Yiming Shi +3
Unsupervised fault detection in multivariate time series plays a vital role in ensuring the stable operation of complex systems. Traditional methods often assume that normal data f…
FGBERT: Function-Driven Pre-trained Gene Language Model for Metagenomics
ChenRui Duan, Zelin Zang, Yongjie Xu +6
Metagenomic data, comprising mixed multi-species genomes, are prevalent in diverse environments like oceans and soils, significantly impacting human health and ecological functions…
Must: Maximizing Latent Capacity of Spatial Transcriptomics Data
Zelin Zang, Liangyu Li, Yongjie Xu +5
Spatial transcriptomics (ST) technologies have revolutionized the study of gene expression patterns in tissues by providing multimodality data in transcriptomic, spatial, and morph…
Deep Manifold Transformation for Protein Representation Learning
Bozhen Hu, Zelin Zang, Cheng Tan +1
Protein representation learning is critical in various tasks in biology, such as drug design and protein structure or function prediction, which has primarily benefited from protei…
Deep Manifold Graph Auto-Encoder for Attributed Graph Embedding
Bozhen Hu, Zelin Zang, Jun Xia +3
Representing graph data in a low-dimensional space for subsequent tasks is the purpose of attributed graph embedding. Most existing neural network approaches learn latent represent…
Graph-level Protein Representation Learning by Structure Knowledge Refinement
Ge Wang, Zelin Zang, Jiangbin Zheng +2
This paper focuses on learning representation on the whole graph level in an unsupervised manner. Learning graph-level representation plays an important role in a variety of real-w…