A HyperGraphMamba-Based Multichannel Adaptive Model for ncRNA Classification

• URL: http://arxiv.org/abs/2509.20240v1
• Date: Wed, 24 Sep 2025 15:31:49 GMT
• Title: A HyperGraphMamba-Based Multichannel Adaptive Model for ncRNA Classification
• Authors: Xin An, Ruijie Li, Qiao Ning, Hui Li, Qian Ma, Shikai Guo,
• Abstract summary: Non-coding RNAs (ncRNAs) play pivotal roles in gene expression regulation and the pathogenesis of various diseases.<n>We propose HGMamba-ncRNA, a HyperGraphMamba-based multichannel adaptive model, which integrates sequence, secondary structure, and expression features to enhance classification performance.<n> Experiments conducted on three public datasets demonstrate that HGMamba-ncRNA consistently outperforms state-of-the-art methods in terms of accuracy and other metrics.
• Score: 7.598192367116628
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Non-coding RNAs (ncRNAs) play pivotal roles in gene expression regulation and the pathogenesis of various diseases. Accurate classification of ncRNAs is essential for functional annotation and disease diagnosis. To address existing limitations in feature extraction depth and multimodal fusion, we propose HGMamba-ncRNA, a HyperGraphMamba-based multichannel adaptive model, which integrates sequence, secondary structure, and optionally available expression features of ncRNAs to enhance classification performance. Specifically, the sequence of ncRNA is modeled using a parallel Multi-scale Convolution and LSTM architecture (MKC-L) to capture both local patterns and long-range dependencies of nucleotides. The structure modality employs a multi-scale graph transformer (MSGraphTransformer) to represent the multi-level topological characteristics of ncRNA secondary structures. The expression modality utilizes a Chebyshev Polynomial-based Kolmogorov-Arnold Network (CPKAN) to effectively model and interpret high-dimensional expression profiles. Finally, by incorporating virtual nodes to facilitate efficient and comprehensive multimodal interaction, HyperGraphMamba is proposed to adaptively align and integrate multichannel heterogeneous modality features. Experiments conducted on three public datasets demonstrate that HGMamba-ncRNA consistently outperforms state-of-the-art methods in terms of accuracy and other metrics. Extensive empirical studies further confirm the model's robustness, effectiveness, and strong transferability, offering a novel and reliable strategy for complex ncRNA functional classification. Code and datasets are available at https://anonymous.4open.science/r/HGMamba-ncRNA-94D0.

Related papers

• CrossLLM-Mamba: Multimodal State Space Fusion of LLMs for RNA Interaction Prediction [4.05599528263557]
  We introduce CrossLLM-Mamba, a novel framework that reformulates interaction prediction as a state-space alignment problem.<n> Comprehensive experiments across three interaction categories, RNA-protein, RNA-small molecule, and RNA-RNA demonstrate that CrossLLM-Mamba achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-02-23T19:57:11Z)
• TRIDENT: A Trimodal Cascade Generative Framework for Drug and RNA-Conditioned Cellular Morphology Synthesis [56.9460577864211]
  TRIDENT is a cascade generative framework that synthesizes realistic cellular morphology by conditioning on both the perturbation and the corresponding gene expression profile.<n> TRIDENT significantly outperforms state-of-the-art approaches, achieving up to 7-fold improvement with strong generalization to unseen compounds.
  arXiv  Detail & Related papers  (2025-11-23T04:43:27Z)
• Bidirectional Representations Augmented Autoregressive Biological Sequence Generation:Application in De Novo Peptide Sequencing [51.12821379640881]
  Autoregressive (AR) models offer holistic, bidirectional representations but face challenges with generative coherence and scalability.<n>We propose a hybrid framework enhancing AR generation by dynamically integrating rich contextual information from non-autoregressive mechanisms.<n>A novel cross-decoder attention module enables the AR decoder to iteratively query and integrate these bidirectional features.
  arXiv  Detail & Related papers  (2025-10-09T12:52:55Z)
• impuTMAE: Multi-modal Transformer with Masked Pre-training for Missing Modalities Imputation in Cancer Survival Prediction [75.43342771863837]
  We introduce impuTMAE, a novel transformer-based end-to-end approach with an efficient multimodal pre-training strategy.<n>It learns inter- and intra-modal interactions while simultaneously imputing missing modalities by reconstructing masked patches.<n>Our model is pre-trained on heterogeneous, incomplete data and fine-tuned for glioma survival prediction using TCGA-GBM/LGG and BraTS datasets.
  arXiv  Detail & Related papers  (2025-08-08T10:01:16Z)
• CodonMoE: DNA Language Models for mRNA Analyses [4.046100165562807]
  Genomic language models (gLMs) face a fundamental efficiency challenge: either maintain separate specialized models for each biological modality (DNA and RNA) or develop large multi-modal architectures.<n>We introduce CodonMoE, a lightweight adapter that transforms DNA language models into effective RNA analyzers without RNA-specific pretraining.<n>Our approach provides a principled path toward unifying genomic language modeling, leveraging more abundant DNA data and reducing computational overhead.
  arXiv  Detail & Related papers  (2025-08-06T01:40:12Z)
• RiboGen: RNA Sequence and Structure Co-Generation with Equivariant MultiFlow [0.0]
  RiboGen is the first deep learning model to simultaneously generate RNA sequence and all-atom 3D structure.<n>Our experiments show that RiboGen can efficiently generate chemically plausible and self-consistent RNA samples.
  arXiv  Detail & Related papers  (2025-03-03T21:19:11Z)
• scMamba: A Pre-Trained Model for Single-Nucleus RNA Sequencing Analysis in Neurodegenerative Disorders [43.24785083027205]
  scMamba is a pre-trained model designed to improve the quality and utility of snRNA-seq analysis.<n>Inspired by the recent Mamba model, scMamba introduces a novel architecture that incorporates a linear adapter layer, gene embeddings, and bidirectional Mamba blocks.<n>We demonstrate that scMamba outperforms benchmark methods in various downstream tasks, including cell type annotation, doublet detection, imputation, and the identification of differentially expressed genes.
  arXiv  Detail & Related papers  (2025-02-12T11:48:22Z)
• DPLM-2: A Multimodal Diffusion Protein Language Model [75.98083311705182]
  We introduce DPLM-2, a multimodal protein foundation model that extends discrete diffusion protein language model (DPLM) to accommodate both sequences and structures. DPLM-2 learns the joint distribution of sequence and structure, as well as their marginals and conditionals. Empirical evaluation shows that DPLM-2 can simultaneously generate highly compatible amino acid sequences and their corresponding 3D structures.
  arXiv  Detail & Related papers  (2024-10-17T17:20:24Z)
• scASDC: Attention Enhanced Structural Deep Clustering for Single-cell RNA-seq Data [5.234149080137045]
  High sparsity and complex noise patterns inherent in scRNA-seq data present significant challenges for traditional clustering methods. We propose a deep clustering method, Attention-Enhanced Structural Deep Embedding Graph Clustering (scASDC) scASDC integrates multiple advanced modules to improve clustering accuracy and robustness.
  arXiv  Detail & Related papers  (2024-08-09T09:10:36Z)
• BEACON: Benchmark for Comprehensive RNA Tasks and Language Models [60.02663015002029]
  We introduce the first comprehensive RNA benchmark BEACON (textbfBEnchmtextbfArk for textbfCOmprehensive RtextbfNA Task and Language Models).<n>First, BEACON comprises 13 distinct tasks derived from extensive previous work covering structural analysis, functional studies, and engineering applications.<n>Second, we examine a range of models, including traditional approaches like CNNs, as well as advanced RNA foundation models based on language models, offering valuable insights into the task-specific performances of these models.<n>Third, we investigate the vital RNA language model components
  arXiv  Detail & Related papers  (2024-06-14T19:39:19Z)
• scBiGNN: Bilevel Graph Representation Learning for Cell Type Classification from Single-cell RNA Sequencing Data [62.87454293046843]
  Graph neural networks (GNNs) have been widely used for automatic cell type classification. scBiGNN comprises two GNN modules to identify cell types. scBiGNN outperforms a variety of existing methods for cell type classification from scRNA-seq data.
  arXiv  Detail & Related papers  (2023-12-16T03:54:26Z)
• Accurate RNA 3D structure prediction using a language model-based deep learning approach [50.193512039121984]
  RhoFold+ is an RNA language model-based deep learning method that accurately predicts 3D structures of single-chain RNAs from sequences.<n>RhoFold+ offers a fully automated end-to-end pipeline for RNA 3D structure prediction.
  arXiv  Detail & Related papers  (2022-07-04T17:15:35Z)
• Classification of Long Noncoding RNA Elements Using Deep Convolutional Neural Networks and Siamese Networks [17.8181080354116]
  This thesis proposes a new methodemploying deep convolutional neural networks (CNNs) to classifyncRNA sequences. As a result, clas-sifying RNA sequences is converted to an image classificationproblem that can be efficiently solved by CNN-basedclassification models.
  arXiv  Detail & Related papers  (2021-02-10T17:26:38Z)
• A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning [49.86828302591469]
  We train >35,000 neural network models, sweeping over common featurization techniques. We found the RNA-seq to be highly redundant and informative even with subsets larger than 128 features.
  arXiv  Detail & Related papers  (2020-04-30T20:42:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.