5 papers · 1 filter
scGTN: Deep Siamese Graph Transformer Network for Single-cell RNA Sequencing Clustering
Jinke Wu, Yifan Wang, Siyu Yi +5
Single-cell RNA sequencing (scRNA-seq) serves a pivotal role in characterizing gene expression at the cellular level, enabling the identification of cell types and advancing the un…
PepALD: Macrocyclic Peptide Generation via Autoregressive Latent Diffusion
Junming Zhang, Siyu Yi, Wei Ju +1
Macrocyclic peptides are promising therapeutic candidates for intracellular targets, but their design requires simultaneous control over non-natural monomer chemistry, ring topolog…
CMGL: Confidence-guided Multi-omics Graph Learning for Cancer Subtype Classification
Boyang Fan, Hengchuang Yin, Siyu Yi +5
Motivation: Multi-omics integration can improve cancer subtyping, but modality informativeness and noise vary across cancer types and patients. Existing graph-based methods optimiz…
Rewarding the Journey, Not Just the Destination: A Composite Path and Answer Self-Scoring Reward Mechanism for Test-Time Reinforcement Learning
Jingyu Xing, Chenwei Tang, Xinyu Liu +5
Reinforcement Learning (RL) has emerged as a powerful paradigm for advancing Large Language Models (LLMs), achieving remarkable performance in complex reasoning domains such as mat…
Bridging the Gap between Learning and Inference for Diffusion-Based Molecule Generation
Peidong Liu, Wenbo Zhang, Wei Ju +2
The paradigm shift toward structure-driven molecule generation has been propelled by advances in deep generative models, such as variational auto-encoders and diffusion models. How…