16 citations · 66 across the 38 of their papers we have counts for
22 papers · 1 filter
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…
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…
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…
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…
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…
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…
DiffAug: Enhance Unsupervised Contrastive Learning with Domain-Knowledge-Free Diffusion-based Data Augmentation
Zelin Zang, Hao Luo, Kai Wang +4
Unsupervised Contrastive learning has gained prominence in fields such as vision, and biology, leveraging predefined positive/negative samples for representation learning. Data aug…
EVNet: An Explainable Deep Network for Dimension Reduction
Zelin Zang, Shenghui Cheng, Linyan Lu +7
Dimension reduction (DR) is commonly utilized to capture the intrinsic structure and transform high-dimensional data into low-dimensional space while retaining meaningful propertie…
DLME: Deep Local-flatness Manifold Embedding
Zelin Zang, Siyuan Li, Di Wu +5
Manifold learning (ML) aims to seek low-dimensional embedding from high-dimensional data. The problem is challenging on real-world datasets, especially with under-sampling data, an…
UDRN: Unified Dimensional Reduction Neural Network for Feature Selection and Feature Projection
Zelin Zang, Yongjie Xu, Linyan Lu +3
Dimensional reduction~(DR) maps high-dimensional data into a lower dimensions latent space with minimized defined optimization objectives. The DR method usually falls into feature…
GenURL: A General Framework for Unsupervised Representation Learning
Siyuan Li, Zicheng Liu, Zelin Zang +3
Unsupervised representation learning (URL), which learns compact embeddings of high-dimensional data without supervision, has made remarkable progress recently. However, the develo…
Unsupervised Deep Manifold Attributed Graph Embedding
Zelin Zang, Siyuan Li, Di Wu +3
Unsupervised attributed graph representation learning is challenging since both structural and feature information are required to be represented in the latent space. Existing meth…
Consistent Representation Learning for High Dimensional Data Analysis
Stan Z. Li, Lirong Wu, Zelin Zang
High dimensional data analysis for exploration and discovery includes three fundamental tasks: dimensionality reduction, clustering, and visualization. When the three associated ta…
Deep Manifold Transformation for Nonlinear Dimensionality Reduction
Stan Z. Li, Zelin Zang, Lirong Wu
Manifold learning-based encoders have been playing important roles in nonlinear dimensionality reduction (NLDR) for data exploration. However, existing methods can often fail to pr…
Invertible Manifold Learning for Dimension Reduction
Siyuan Li, Haitao Lin, Zelin Zang +3
Dimension reduction (DR) aims to learn low-dimensional representations of high-dimensional data with the preservation of essential information. In the context of manifold learning,…
Generalized Clustering and Multi-Manifold Learning with Geometric Structure Preservation
Lirong Wu, Zicheng Liu, Zelin Zang +3
Though manifold-based clustering has become a popular research topic, we observe that one important factor has been omitted by these works, namely that the defined clustering loss…