Auxiliary Gene Learning: Spatial Gene Expression Estimation by Auxiliary Gene Selection
Fuente:
arXiv
Saved in:
| Main Authors: | Shiku, Kaito, Nishimura, Kazuya, Matsuo, Shinnosuke, Kojima, Yasuhiro, Bise, Ryoma |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Learning Relative Gene Expression Trends from Pathology Images in Spatial Transcriptomics
by: Nishimura, Kazuya, et al.
Published: (2025)
by: Nishimura, Kazuya, et al.
Published: (2025)
Cell-Type Prototype-Informed Neural Network for Gene Expression Estimation from Pathology Images
by: Nishimura, Kazuya, et al.
Published: (2026)
by: Nishimura, Kazuya, et al.
Published: (2026)
Counting Network for Learning from Majority Label
by: Shiku, Kaito, et al.
Published: (2024)
by: Shiku, Kaito, et al.
Published: (2024)
Ordinal Multiple-instance Learning for Ulcerative Colitis Severity Estimation with Selective Aggregated Transformer
by: Shiku, Kaito, et al.
Published: (2024)
by: Shiku, Kaito, et al.
Published: (2024)
Leveraging Spatial Transcriptomics as Alternative to Manual Annotations for Deep Learning-Based Nuclei Analysis
by: Nishimura, Kazuya, et al.
Published: (2026)
by: Nishimura, Kazuya, et al.
Published: (2026)
Learning from Majority Label: A Novel Problem in Multi-class Multiple-Instance Learning
by: Kaito, Shiku, et al.
Published: (2025)
by: Kaito, Shiku, et al.
Published: (2025)
Evaluating Deep Regression Models for WSI-Based Gene-Expression Prediction
by: Gustafsson, Fredrik K., et al.
Published: (2024)
by: Gustafsson, Fredrik K., et al.
Published: (2024)
Towards Spatial Transcriptomics-guided Pathological Image Recognition with Batch-Agnostic Encoder
by: Nishimura, Kazuya, et al.
Published: (2025)
by: Nishimura, Kazuya, et al.
Published: (2025)
CausalGeD: Blending Causality and Diffusion for Spatial Gene Expression Generation
by: Sadia, Rabeya Tus, et al.
Published: (2025)
by: Sadia, Rabeya Tus, et al.
Published: (2025)
Leveraging Vision-Language Models as Weak Annotators in Active Learning
by: Nguyen, Phuong Ngoc, et al.
Published: (2026)
by: Nguyen, Phuong Ngoc, et al.
Published: (2026)
A Large-Scale Benchmark of Cross-Modal Learning for Histology and Gene Expression in Spatial Transcriptomics
by: Gindra, Rushin H., et al.
Published: (2025)
by: Gindra, Rushin H., et al.
Published: (2025)
GC-MoE: Genomics-Guided Cell-Type-Specific Mixture of Experts for Histology-Based Single-Cell Spatial Transcriptomics
by: Shiku, Kaito, et al.
Published: (2026)
by: Shiku, Kaito, et al.
Published: (2026)
Leveraging Label Proportion Prior for Class-Imbalanced Semi-Supervised Learning
by: Akiba, Kohki, et al.
Published: (2026)
by: Akiba, Kohki, et al.
Published: (2026)
ST-Align: A Multimodal Foundation Model for Image-Gene Alignment in Spatial Transcriptomics
by: Lin, Yuxiang, et al.
Published: (2024)
by: Lin, Yuxiang, et al.
Published: (2024)
Advancing Gene Selection in Oncology: A Fusion of Deep Learning and Sparsity for Precision Gene Selection
by: Krishna, Akhila, et al.
Published: (2024)
by: Krishna, Akhila, et al.
Published: (2024)
Instance-wise Supervision-level Optimization in Active Learning
by: Matsuo, Shinnosuke, et al.
Published: (2025)
by: Matsuo, Shinnosuke, et al.
Published: (2025)
Proportion Estimation by Masked Learning from Label Proportion
by: Okuo, Takumi, et al.
Published: (2024)
by: Okuo, Takumi, et al.
Published: (2024)
NoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models
by: Takezaki, Shumpei, et al.
Published: (2025)
by: Takezaki, Shumpei, et al.
Published: (2025)
Cell Tracking in C. elegans with Cell Position Heatmap-Based Alignment and Pairwise Detection
by: Shiku, Kaito, et al.
Published: (2024)
by: Shiku, Kaito, et al.
Published: (2024)
Hypernetwork-Based Adaptive Aggregation for Multimodal Multiple-Instance Learning in Predicting Coronary Calcium Debulking
by: Shiku, Kaito, et al.
Published: (2026)
by: Shiku, Kaito, et al.
Published: (2026)
Path-GPTOmic: A Balanced Multi-modal Learning Framework for Survival Outcome Prediction
by: Wang, Hongxiao, et al.
Published: (2024)
by: Wang, Hongxiao, et al.
Published: (2024)
A Deep Learning Pipeline for Epilepsy Genomic Analysis Using GPT-2 XL and NVIDIA H100
by: Latif, Muhammad Omer, et al.
Published: (2025)
by: Latif, Muhammad Omer, et al.
Published: (2025)
Beyond Independent Genes: Learning Module-Inductive Representations for Gene Perturbation Prediction
by: Ruan, Jiafa, et al.
Published: (2026)
by: Ruan, Jiafa, et al.
Published: (2026)
A Comparative Analysis of Gene Expression Profiling by Statistical and Machine Learning Approaches
by: Bontonou, Myriam, et al.
Published: (2024)
by: Bontonou, Myriam, et al.
Published: (2024)
Multi-omic Causal Discovery using Genotypes and Gene Expression
by: Asiedu, Stephen, et al.
Published: (2025)
by: Asiedu, Stephen, et al.
Published: (2025)
Modeling Gene Expression Distributional Shifts for Unseen Genetic Perturbations
by: Ramakrishnan, Kalyan, et al.
Published: (2025)
by: Ramakrishnan, Kalyan, et al.
Published: (2025)
A Standardized Framework For Evaluating Gene Expression Generative Models
by: Rubbi, Andrea, et al.
Published: (2026)
by: Rubbi, Andrea, et al.
Published: (2026)
On the Recoverability of Causal Relations from Bulk Gene Expression Data
by: Luo, Gongxu, et al.
Published: (2026)
by: Luo, Gongxu, et al.
Published: (2026)
Leveraging State Space Models in Long Range Genomics
by: Popov, Matvei, et al.
Published: (2025)
by: Popov, Matvei, et al.
Published: (2025)
Explainable AI model reveals disease-related mechanisms in single-cell RNA-seq data
by: Usman, Mohammad, et al.
Published: (2025)
by: Usman, Mohammad, et al.
Published: (2025)
CrossLLM-Mamba: Multimodal State Space Fusion of LLMs for RNA Interaction Prediction
by: Sadia, Rabeya Tus, et al.
Published: (2026)
by: Sadia, Rabeya Tus, et al.
Published: (2026)
Machine Learning-Based Analysis of Ebola Virus' Impact on Gene Expression in Nonhuman Primates
by: Rezapour, Mostafa, et al.
Published: (2024)
by: Rezapour, Mostafa, et al.
Published: (2024)
Scalable Single-Cell Gene Expression Generation with Latent Diffusion Models
by: Palla, Giovanni, et al.
Published: (2025)
by: Palla, Giovanni, et al.
Published: (2025)
HEXST: Hexagonal Shifted-Window Transformer for Spatial Transcriptomics Gene Expression Prediction
by: Byeon, Keunho, et al.
Published: (2026)
by: Byeon, Keunho, et al.
Published: (2026)
SpaDiT: Diffusion Transformer for Spatial Gene Expression Prediction using scRNA-seq
by: Li, Xiaoyu, et al.
Published: (2024)
by: Li, Xiaoyu, et al.
Published: (2024)
Deep Pathomic Learning Defines Prognostic Subtypes and Molecular Drivers in Colorectal Cancer
by: Wang, Zisong, et al.
Published: (2025)
by: Wang, Zisong, et al.
Published: (2025)
Multi-omics Prediction from High-content Cellular Imaging with Deep Learning
by: Mehrizi, Rahil, et al.
Published: (2023)
by: Mehrizi, Rahil, et al.
Published: (2023)
Meta-Learning on Augmented Gene Expression Profiles for Enhanced Lung Cancer Detection
by: Moghaddam, Arya Hadizadeh, et al.
Published: (2024)
by: Moghaddam, Arya Hadizadeh, et al.
Published: (2024)
Generative Language Models on Nucleotide Sequences of Human Genes
by: Ihtiyar, Musa Nuri, et al.
Published: (2023)
by: Ihtiyar, Musa Nuri, et al.
Published: (2023)
Domain Adaptation for Ulcerative Colitis Severity Estimation Using Patient-Level Diagnoses
by: Yamaguchi, Takamasa, et al.
Published: (2025)
by: Yamaguchi, Takamasa, et al.
Published: (2025)
Similar Items
-
Learning Relative Gene Expression Trends from Pathology Images in Spatial Transcriptomics
by: Nishimura, Kazuya, et al.
Published: (2025) -
Cell-Type Prototype-Informed Neural Network for Gene Expression Estimation from Pathology Images
by: Nishimura, Kazuya, et al.
Published: (2026) -
Counting Network for Learning from Majority Label
by: Shiku, Kaito, et al.
Published: (2024) -
Ordinal Multiple-instance Learning for Ulcerative Colitis Severity Estimation with Selective Aggregated Transformer
by: Shiku, Kaito, et al.
Published: (2024) -
Leveraging Spatial Transcriptomics as Alternative to Manual Annotations for Deep Learning-Based Nuclei Analysis
by: Nishimura, Kazuya, et al.
Published: (2026)