Paper List
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Pharmacophore-based design by learning on voxel grids
This paper addresses the computational bottleneck and limited novelty in conventional pharmacophore-based virtual screening by introducing a voxel cap...
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CONFIDE: Hallucination Assessment for Reliable Biomolecular Structure Prediction and Design
This paper addresses the critical limitation of current protein structure prediction models (like AlphaFold3) where high-confidence scores (pLDDT) can...
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On the Approximation of Phylogenetic Distance Functions by Artificial Neural Networks
This paper addresses the core challenge of developing computationally efficient and scalable neural network architectures that can learn accurate phyl...
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EcoCast: A Spatio-Temporal Model for Continual Biodiversity and Climate Risk Forecasting
This paper addresses the critical bottleneck in conservation: the lack of timely, high-resolution, near-term forecasts of species distribution shifts ...
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Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
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Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
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Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
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scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
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.