RNA-FM: Flow-Matching Generative Model for Genome-wide RNA-Seq Prediction

• URL: http://arxiv.org/abs/2605.11622v1
• Date: Tue, 12 May 2026 06:48:58 GMT
• Title: RNA-FM: Flow-Matching Generative Model for Genome-wide RNA-Seq Prediction
• Abstract summary: We propose RNA-FM, a flow-matching generative framework for genome-wide bulk RNA-seq prediction from whole-slide images.<n> RNA-FM formulates transcriptomic prediction as a continuous-time conditional transport problem.<n>It enables scalable and biologically interpretable genome-wide gene expression imputation.
• Score: 48.07329582311906
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Histopathology whole-slide images (WSIs) are routinely acquired in clinical practice and contain rich tissue morphology but lack direct molecular architecture and functional programs defining pathological states, whereas RNA sequencing (RNA-seq) provides genome-wide transcriptional profiles at substantial cost, thereby motivating WSI-based genome-wide transcriptomic prediction. Existing approaches for predicting gene expression from WSIs predominantly rely on deterministic regression with one-to-one mapping, limiting their ability to capture biological heterogeneity and predictive uncertainty. We propose RNA-FM, a flow-matching generative framework for genome-wide bulk RNA-seq prediction from WSIs. RNA-FM formulates transcriptomic prediction as a continuous-time conditional transport problem, learning a velocity field that maps a simple prior to the target gene expression distribution conditioned on morphologies. By integrating pathway-level structure, RNA-FM enables scalable and biologically interpretable genome-wide gene expression imputation. Extensive experiments demonstrate that RNA-FM consistently outperforms state-of-the-art approaches while maintaining biological meaningfulness. Code is available at https://github.com/YXSong000/RNA-FM.

Related papers

• RNAMunin: A Deep Machine Learning Model for Non-coding RNA Discovery [0.0]
  Non-coding RNAs (ncRNAs) are critical for regulating bacterial and archaeal physiology, stress response and metabolism.<n>This paper presents RNAMunin, a machine learning (ML) model that is capable of finding ncRNAs using genomic sequence alone.<n> RNAMunin is trained on Rfam sequences extracted from approximately 60 Gbp of long read metagenomes from 16 San Francisco Estuary samples.
  arXiv  Detail & Related papers  (2025-07-16T06:33:50Z)
• CircFormerMoE: An End-to-End Deep Learning Framework for Circular RNA Splice Site Detection and Pairing in Plant Genomes [0.0]
  Circular RNAs (circRNAs) are important components of the non-coding RNA regulatory network.<n>We propose a deep learning framework named CircFormerMoE based on transformers and mixture-of experts for predicting circRNAs directly from plant genomic DNA.
  arXiv  Detail & Related papers  (2025-07-11T12:43:17Z)
• GRAPE: Heterogeneous Graph Representation Learning for Genetic Perturbation with Coding and Non-Coding Biotype [51.58774936662233]
  Building gene regulatory networks (GRN) is essential to understand and predict the effects of genetic perturbations.<n>In this work, we leverage pre-trained large language model and DNA sequence model to extract features from gene descriptions and DNA sequence data.<n>We introduce gene biotype information for the first time in genetic perturbation, simulating the distinct roles of genes with different biotypes in regulating cellular processes.
  arXiv  Detail & Related papers  (2025-05-06T03:35:24Z)
• A Comprehensive Review on RNA Subcellular Localization Prediction [0.125828876338076]
  Long non-coding RNAs (lncRNAs), messenger RNAs (mRNAs), microRNAs (miRNAs) and other RNAs play a critical role in determining their biological functions.<n>Traditional wet lab methods for determining RNA localization, such as in situ hybridization, are often time-consuming, resource-demanding, and costly.<n> Computational methods leveraging artificial intelligence (AI) and machine learning (ML) have emerged as powerful alternatives.
  arXiv  Detail & Related papers  (2025-04-24T00:47:31Z)
• GENERator: A Long-Context Generative Genomic Foundation Model [66.46537421135996]
  We present GENERator, a generative genomic foundation model featuring a context length of 98k base pairs (bp) and 1.2B parameters.<n>Trained on an expansive dataset comprising 386B bp of DNA, the GENERator demonstrates state-of-the-art performance across both established and newly proposed benchmarks.<n>It also shows significant promise in sequence optimization, particularly through the prompt-responsive generation of enhancer sequences with specific activity profiles.
  arXiv  Detail & Related papers  (2025-02-11T05:39:49Z)
• Character-level Tokenizations as Powerful Inductive Biases for RNA Foundational Models [0.0]
  understanding and predicting RNA behavior is a challenge due to the complexity of RNA structures and interactions. Current RNA models have yet to match the performance observed in the protein domain. ChaRNABERT is able to reach state-of-the-art performance on several tasks in established benchmarks.
  arXiv  Detail & Related papers  (2024-11-05T21:56:16Z)
• RNACG: A Universal RNA Sequence Conditional Generation model based on Flow-Matching [0.0]
  We propose RNACG (RNA Generator), a universal framework for RNA sequence design based on flow matching.<n>By unifying sequence generation under a single framework, RNACG enables the integration of multiple RNA design paradigms.
  arXiv  Detail & Related papers  (2024-07-29T09:46:46Z)
• VQDNA: Unleashing the Power of Vector Quantization for Multi-Species Genomic Sequence Modeling [60.91599380893732]
  VQDNA is a general-purpose framework that renovates genome tokenization from the perspective of genome vocabulary learning. By leveraging vector-quantized codebooks as learnable vocabulary, VQDNA can adaptively tokenize genomes into pattern-aware embeddings.
  arXiv  Detail & Related papers  (2024-05-13T20:15:03Z)
• scHyena: Foundation Model for Full-Length Single-Cell RNA-Seq Analysis in Brain [46.39828178736219]
  We introduce scHyena, a foundation model designed to address these challenges and enhance the accuracy of scRNA-seq analysis in the brain. scHyena is equipped with a linear adaptor layer, the positional encoding via gene-embedding, and a bidirectional Hyena operator. This enables us to process full-length scRNA-seq data without losing any information from the raw data.
  arXiv  Detail & Related papers  (2023-10-04T10:30:08Z)
• 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)

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.