Author

Kai-Li Chang

Baylor College of MedicineORCID 0000-0002-1648-8755Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:benchmarking· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Research Focus:perturbation response prediction· 1 Assay:scRNA-seq· 1 Approach:statistical model· 1 Research Focus:trajectory inference· 1

Frequent Co-Authors

Zhandong LiuiD· 2 shared Athanasios Papastathopoulos-KatsarosiD· 1 shared Hu CheniD· 1 shared Ilia BuralkiniD· 1 shared Jorge BotasiD· 1 shared Junseok ParkiD· 1 shared Yueqian DengiD· 1 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 16 mutual co-author links

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2 shared papersBioinformatics

Zhandong LiuORCID

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2 papers

Recent·Most cited

BioinformaticsbioRxivPreprintMay 26, 2026

Vision-Based Genomic Model for Copy Number Variant Pathogenicity Prediction

Ilia Buralkin, Jorge Botas, Kai-Li Chang…·Junseok Park

This paper introduces a vision-based model for predicting CNV pathogenicity using track-based spatial representations, achieving improved performance on benchmarks and clinical datasets.

DOI 10.64898/2026.05.21.726953

whole-genome sequencingdeep learning

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BioinformaticsbioRxivPreprintDec 12, 2025

Reinforcement learning enables single-cell foundation models to learn cellular differentiation

Kai-Li Chang, Hu Chen, Zhandong Liu

This paper introduces DiRL, a reinforcement learning framework that enables single-cell foundation models to model and control cellular differentiation by optimizing multi-step perturbation strategies.

1 citation

humanscRNA-seq

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