7 papers
Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control
Shaorong Chen, Jingbo Zhou, Jun Xia
The discovery of novel proteins relies on sensitive protein identification, for which de novo peptide sequencing (DNPS) from mass spectra is a crucial approach. While deep learning…
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…
MergeDNA: Context-aware Genome Modeling with Dynamic Tokenization through Token Merging
Siyuan Li, Kai Yu, Anna Wang +7
Modeling genomic sequences faces two unsolved challenges: the information density varies widely across different regions, while there is no clearly defined minimum vocabulary unit.…
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…
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…