11 papers
Towards Reasonable Molecular Structure Elucidation from Infrared Spectroscopy with Chemical Feedback
Yusen Tan, Hongyu Zhan, Hai-tao Yu +3
Infrared (IR) spectra provide characteristic signals of molecular structure, which are often interpreted by experts via functional-group identification or library matching, making…
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…
Improving Diffusion Generalization with Weak-to-Strong Segmented Guidance
Liangyu Yuan, Yufei Huang, Mingkun Lei +5
Diffusion models generate synthetic images through an iterative refinement process. However, the misalignment between the simulation-free objective and the iterative process often…
Refold: Refining Protein Inverse Folding with Efficient Structural Matching and Fusion
Yiran Zhu, Changxi Chi, Hongxin Xiang +3
Protein inverse folding aims to design an amino acid sequence that will fold into a given backbone structure, serving as a central task in protein design. Two main paradigms have b…
VecFormer: Towards Efficient and Generalizable Graph Transformer with Graph Token Attention
Jingbo Zhou, Jun Xia, Siyuan Li +11
Graph Transformer has demonstrated impressive capabilities in the field of graph representation learning. However, existing approaches face two critical challenges: (1) most models…
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…
PRESCRIBE: Predicting Single-Cell Responses with Bayesian Estimation
Jiabei Cheng, Changxi Chi, Jingbo Zhou +2
In single-cell perturbation prediction, a central task is to forecast the effects of perturbing a gene unseen in the training data. The efficacy of such predictions depends on two…
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…
GRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype
Changxi Chi, Jun Xia, Jingbo Zhou +3
Predicting genetic perturbations enables the identification of potentially crucial genes prior to wet-lab experiments, significantly improving overall experimental efficiency. Sinc…