Hugging Face Trending Papers July 23, 2026

M$^3$-Gen: Interpretable Multimodal Generation of Gene Expression Profiles Using Clinical and Imaging Data

Read the original on Hugging Face Trending Papers →

Integrating heterogeneous biomedical data, including clinical metadata, histopathology images, and molecular profiles, is crucial for comprehensive disease understanding. However, gene expression data acquisition remains constrained by high costs and privacy concerns, limiting its use in multimodal research and AI-driven applications.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

arXiv AI

Jul 24

M$^3$-Gen: Interpretable Multimodal Generation of Gene Expression Profiles Using Clinical and Imaging Data

arXiv:2607. 21343v1 Announce Type: cross Abstract: Integrating heterogeneous biomedical data, including clinical metadata, histopathology images, and molecular profiles, is crucial for comprehensive disease understanding.

By Francesca Pia Panaccione, Carlo Sgaravatti, Marco Venere

rag multimodal safety

arXiv AI

6d ago

Novel Knowledge-Guided Generative Methods for Synthetic Transcriptomic Data

arXiv:2608. 13256v1 Announce Type: cross Abstract: As biomedical research increasingly relies on data-intensive tools, the quality and utility of datasets are critical.

By Francesca Pia Panaccione, Sofia Mongardi, Marco Masseroli, Pietro Pinoli

benchmarks safety

arXiv Machine Learning

Jul 3

CALM: Interpretable Cross-Modal Alignment for Biomarker Discovery from Unpaired Data

arXiv:2607. 01656v1 Announce Type: new Abstract: The interaction between brain structure and genetic influences is key to understanding neuropsychiatric disorders.

By Jueqi Wang, Zachary Jacokes, John Darrell Van Horn, Kevin A. Pelphrey, Michael C. Schatz, Archana Venkataraman

diffusion multimodal benchmarks safety

arXiv AI

Jun 17

Probing, Fusion, and Trustworthiness: A Systematic Evaluation of Foundation Model Representations for Multimodal Cancer Analysis

arXiv:2606. 17115v1 Announce Type: cross Abstract: Foundation models (FMs) have emerged as powerful representation extractors for medical data, yet their generalizability to datasets under distribution shift remains underexplored.

By Jingyu Hu, Giuseppe Tripodi, Reed Naidoo, Sarah F. McGough, Tapabrata Chakraborti

multimodal benchmarks

arXiv AI

Jun 18

Learning Patient-Specific Disease Dynamics with Latent Flow Matching for Longitudinal Imaging Generation

arXiv:2512. 09185v4 Announce Type: replace-cross Abstract: Understanding disease progression is a central clinical challenge with direct implications for early diagnosis and personalized treatment.

By Hao Chen, Rui Yin, Yifan Chen, Qi Chen, Chao Li

diffusion benchmarks safety

arXiv AI

Jun 9

No Free Lunch for Synthetic Images under Data Scarcity Conditions

arXiv:2606. 07640v1 Announce Type: cross Abstract: This study investigates the trade-offs between fidelity, privacy, and utility in synthetic data generation under conditions of data scarcity and privacy sensitivity.

By Borja Arroyo Galende, Alejandro Almod\'ovar, Patricia A. Apell\'aniz, Juan Parras, Silvia Uribe, Santiago Zazo

diffusion safety

Related stories

M$^3$-Gen: Interpretable Multimodal Generation of Gene Expression Profiles Using Clinical and Imaging Data

Novel Knowledge-Guided Generative Methods for Synthetic Transcriptomic Data

CALM: Interpretable Cross-Modal Alignment for Biomarker Discovery from Unpaired Data

Probing, Fusion, and Trustworthiness: A Systematic Evaluation of Foundation Model Representations for Multimodal Cancer Analysis

Learning Patient-Specific Disease Dynamics with Latent Flow Matching for Longitudinal Imaging Generation

No Free Lunch for Synthetic Images under Data Scarcity Conditions