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CellxPert integrates multi-omics data for advanced single-cell analysis and perturbation prediction

Researchers have developed CellxPert, a novel multimodal foundation model designed to unify and analyze single-cell and spatial multi-omics data. This model integrates various data types including transcriptomic, chromatin-accessibility, and proteomic measurements, along with spatial imaging data. CellxPert offers capabilities for cell-type annotation, efficient fine-tuning, and predicting genome-wide transcriptomic responses to in-silico perturbations using a Markov-chain sampling method to ensure biological interpretability. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Introduces a new foundation model for multi-omics data analysis, potentially advancing biological research and drug discovery.

RANK_REASON This is a research paper describing a new model and its capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

COVERAGE [1]

  1. arXiv cs.AI TIER_1 · Andac Demir, Erik W. Anderson, Jeremy L. Jenkins, Srayanta Mukherjee ·

    CellxPert: Inference-Time MCMC Steering of a Multi-Omics Single-Cell Foundation Model for In-Silico Perturbation

    arXiv:2605.00930v1 Announce Type: cross Abstract: In this work, we introduce CellxPert, a scalable multimodal foundation model that unifies single-cell and spatial multi-omics within a common representation space. CellxPert jointly encodes transcriptomic (scRNA-seq), chromatin-ac…