Paper List
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Exactly Solvable Population Model with Square-Root Growth Noise and Cell-Size Regulation
This paper addresses the fundamental gap in understanding how microscopic growth fluctuations, specifically those with size-dependent (square-root) no...
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Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models
This paper addresses the core challenge of performing accurate Bayesian parameter inference for stochastic epidemic models when the likelihood functio...
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Realistic Transition Paths for Large Biomolecular Systems: A Langevin Bridge Approach
This paper addresses the core challenge of generating physically realistic and computationally efficient transition paths between distinct protein con...
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MoRSAIK: Sequence Motif Reactor Simulation, Analysis and Inference Kit in Python
This work addresses the computational bottleneck in simulating prebiotic RNA reactor dynamics by developing a Python package that tracks sequence moti...
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The BEAT-CF Causal Model: A model for guiding the design of trials and observational analyses of cystic fibrosis exacerbations
This paper addresses the critical gap in cystic fibrosis exacerbation management by providing a formal causal framework that integrates expert knowled...
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A Theoretical Framework for the Formation of Large Animal Groups: Topological Coordination, Subgroup Merging, and Velocity Inheritance
This paper addresses the core problem of how large, coordinated animal groups form in nature, challenging the classical view of gradual aggregation by...
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ANNE Apnea Paper
This paper addresses the core challenge of achieving accurate, event-level sleep apnea detection and characterization using a non-intrusive, multimoda...
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DeeDeeExperiment: Building an infrastructure for integrating and managing omics data analysis results in R/Bioconductor
This paper addresses the critical bottleneck of managing and organizing the growing volume of differential expression and functional enrichment analys...
PanFoMa: A Lightweight Foundation Model and Benchmark for Pan-Cancer
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The 30-Second View
IN SHORT: This paper addresses the dual challenge of achieving computational efficiency without sacrificing accuracy in whole-transcriptome single-cell representation learning for pan-cancer analysis, moving beyond the limitations of pure Transformer or Mamba architectures.
Innovation (TL;DR)
- Methodology Proposes a novel hybrid architecture (PanFoMa) that decouples local gene interaction modeling (via a lightweight, chunked Transformer encoder) from global context integration (via a bidirectional Mamba decoder), achieving O(C·M² + N log N) complexity.
- Methodology Introduces a Global-informed Dynamic Sorting (GDS) mechanism that adaptively orders genes for the Mamba decoder based on a learned global cell state vector, moving beyond static, heuristic gene ordering (e.g., by mean expression).
- Biology Constructs and releases PanFoMaBench, a large-scale, rigorously curated pan-cancer single-cell benchmark comprising over 3.5 million high-quality cells across 33 cancer subtypes from 23 tissues, addressing the lack of comprehensive evaluation resources.
Key conclusions
- PanFoMa achieves state-of-the-art pan-cancer classification accuracy of 94.74% (ACC) and 92.5% (Macro-F1) on PanFoMaBench, outperforming GeneFormer by +3.5% ACC and +4.0% F1.
- The model demonstrates superior generalizability across foundational tasks, showing improvements of +7.4% in cell type annotation, +4.0% in batch integration, and +3.1% in multi-omics integration over baselines.
- The hybrid local-global design and dynamic sorting are validated as effective, enabling efficient processing of full transcriptome-scale data (~3000 genes) while capturing both fine-grained local interactions and broad global regulatory patterns.
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 PanFoMa, a lightweight hybrid neural network that combines the strengths of Transformers and state-space models to achieve a balance between performance and efficiency. PanFoMa 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, PanFoMaBench, containing over 3.5 million high-quality cells across 33 cancer subtypes, curated through a rigorous preprocessing pipeline. Experimental results show that PanFoMa 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%). The code is available at https://github.com/Xiaoshui-Huang/PanFoMa.