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Wen-Jing Ma

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Open access Oct 2026

How Much Can AI Virtual Cells Gain from Unified Perturbational Modeling?

This work introduces three unification modules into a variational autoencoder (VAE)-based AIVC predictor to address these sources of heterogeneity: gene-channel alignment, shared perturbation encoding, and joint embedding of gene expression and identity.

Zong-Ting Wei, Yue-Dian Ye, Shu-Guang Cui et al. · 0 citations
Book Open access Aug 2026

Concord: Building Consensus Representations for Single Cells with Collaborative Random Projection

Concord is a novel single-cell FM that explicitly models relationships between gene identities and expression levels through a collaborative rotary attention (CRA) mechanism, and employs two collaborative attention modes: a gene-to-expression rotational attention that produces gene-enhanced expression representations,...

Kaichen Xu, Mianpeng Liu, Hao Wu et al. · 0 citations

TORC: Target-Oriented Reference Construction for Supervised Cell-Type Identification in scRNA-seq.

This work presents Target-Oriented Reference Construction (TORC), a widely applicable strategy for constructing reference data from available labeled cells given a target dataset that alleviates the differences in data distribution and cell-type composition between the reference and the target.

Xin Wei, Wen-Jing Ma, Zhi-Jin Wu et al. · 1 citation
Book Open access Aug 2026

Concord: Building Consensus Representations for Single Cells with Collaborative Random Projection

Foundation models (FMs) have recently transformed single-cell genomics by learning transferable representations from large-scale single-cell data, enabling a wide range of downstream biomedical applications. Inspired by natural language processing, existing single-cell FMs adapt transformer architectures by treating ge...

Kaichen Xu, Mianpeng Liu, Hao Wu et al. · 0 citations

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