惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

罗
罗磊的独立博客
G
Google Developers Blog
钛媒体：引领未来商业与生活新知
钛媒体：引领未来商业与生活新知
腾
腾讯CDC
有赞技术团队
有赞技术团队
Vercel News
Vercel News
MongoDB | Blog
MongoDB | Blog
M
MIT News - Artificial intelligence
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
B
Blog RSS Feed
I
InfoQ
Blog — PlanetScale
Blog — PlanetScale
博
博客园_首页
The Cloudflare Blog
B
Blog
C
Check Point Blog
Stack Overflow Blog
Stack Overflow Blog
IT之家
IT之家
U
Unit 42
D
Docker
月光博客
月光博客
aimingoo的专栏
aimingoo的专栏
博
博客园 - Franky
A
About on SuperTechFans

Proceedings of the AAAI Conference on Artificial Intelligence

Resource Efficient Sleep Staging via Multi-Level Masking and Prompt Learning AutoMalDesc: Large-Scale Script Analysis for Cyber Threat Research Modulation-Based Backdoors: Leveraging Amplitude and Frequency Patterns to Attack Speaker Recognition Learning Structurally Stabilized Representations for Lossless DNA Storage ViG-RAG: Video-aware Graph Retrieval-Augmented Generation via Temporal and Semantic Hybrid Reasoning Transferable Backdoor Attacks for Code Models via Sharpness-Aware Adversarial Perturbation Toward Multimodal Fake News Detection by Multi-perspective Rationale Generation and Verification RTMol: Rethinking Molecule-text Alignment in a Round-trip View Physical-regularized Hierarchical Generative Model for Metallic Glass Structural Generation and Energy Prediction Regressor-guided Diffusion Model for De Novo Peptide Sequencing with Explicit Mass Control RareAgents: Autonomous Multi-disciplinary Team for Rare Disease Diagnosis and Treatment Transferring Causal Driving Patterns for Generalizable Traffic Simulation with Diffusion-Based Distillation TRACE: Transformation-Aware Graph Refinement for Reaction Condition Prediction SIDE: Surrogate Conditional Data Extraction from Diffusion Models DyC-STG: Dynamic Causal Spatio-Temporal Graph Network for Real-time Data Credibility Analysis in IoT ProAR: Probabilistic Autoregressive Modeling for Molecular Dynamics Light but Sharp: SlimSTAD for Real-Time Action Detection from Sensor Data VFCionX: Bridging Large and Small Models for Robust Vulnerability-Fixing Commit Identification T2Agent: A Tool-augmented Multimodal Misinformation Detection Agent with Monte Carlo Tree Search Measuring What Matters: Scenario-Driven Evaluation for Trajectory Predictors in Autonomous Driving DensiCrafter: Physically-Constrained Generation and Fabrication of Self-Supporting Hollow Structures Topology-Enhanced and Label Correlation-Aware Model for Protein-Protein Interaction Prediction InteChar: A Unified Oracle Bone Character List for Ancient Chinese Language Modeling NucEL: Single-Nucleotide ELECTRA-Style Genomic Pre-training for Efficient and Interpretable Representations OR-R1: Automating Modeling and Solving of Operations Research Optimization Problem via Test-Time Reinforcement Learning Learning from Long-Term Engagement: Adaptive Tutoring Dialogue Planning for Personalized Education Toward Time-Continuous Data Inference in Sparse Urban CrowdSensing Multi-Horizon Time Series Forecasting of Non-Parametric CDFs with Deep Lattice Networks From Stimuli to Minds: Enhancing Psychological Reasoning in LLMs via Bilateral Reinforcement Learning Unveiling the Attribute Misbinding Threat in Identity-Preserving Models
PanFoMa: A Lightweight Foundation Model and Benchmark for...
Xiaoshui Hua · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Xiaoshui Huang Shanghai Jiaotong University
  • Tianlin Zhu Jiangxi University of Finance and Economics
  • Yifan Zuo Jiangxi University of Finance and Economics
  • Xue Xia Jiangxi University of Finance and Economics
  • Zonghan Wu East China Normal University
  • Jiebin Yan Jiangxi University of Finance and Economics
  • Dingli Hua Jiangxi University of Finance and Economics
  • Zongyi Xu Chongqing University of Post and Telecommunications
  • Yuming Fang Jiangxi University of Finance and Economics
  • Jian Zhang University of Technology Sydney

DOI:

https://doi.org/10.1609/aaai.v40i1.37008

Abstract

Single-cell RNA sequencing (scRNA-seq) is essential for decoding tumor heterogeneity. However, pan-cancer research still faces two key challenges: learning discriminative and efficient single-cell representations, and establishing a comprehensive evaluation benchmark. In this paper, we introduce \algoname, a lightweight hybrid neural network that combines the strengths of Transformers and state-space models to achieve a balance between performance and efficiency. \algoname consists of a front-end local-context encoder with shared self-attention layers to capture complex, order-independent gene interactions; and a back-end global sequential feature decoder that efficiently integrates global context using a linear-time state-space model. This modular design preserves the expressive power of Transformers while leveraging the scalability of Mamba to enable transcriptome modeling, effectively capturing both local and global regulatory signals. To enable robust evaluation, we also construct a large-scale pan-cancer single-cell benchmark, \algoname Bench, containing over 3.5 million high-quality cells across 33 cancer subtypes, curated through a rigorous preprocessing pipeline. Experimental results show that \algoname outperforms state-of-the-art models on our pan-cancer benchmark (+4.0\%) and across multiple public tasks, including cell type annotation (+7.4\%), batch integration (+4.0\%) and multi-omics integration (+3.1\%).

How to Cite

Huang, X., Zhu, T., Zuo, Y., Xia, X., Wu, Z., Yan, J., … Zhang, J. (2026). PanFoMa: A Lightweight Foundation Model and Benchmark for Pan-Cancer. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 453–461. https://doi.org/10.1609/aaai.v40i1.37008

Issue

Section

AAAI Technical Track on Application Domains I

此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。