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Yingtao Luo on Muck Rack

Yingtao Luo

As seen in: Nature, arXiv, BioRxiv, Physical Review B
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Articles

scLong: a billion-parameter foundation model for capturing long-range gene context in single-cell transcriptomics - Nature Communications

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By Ding Bai, Yingtao Luo, Hamidreza Rahmani, Danielle A. Grotjahn, Nathan Lewis, Wei Wang, Trey Ideker, Pengtao XiePengtao Xie
| Nature Verified
Abstract Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of cellular heterogeneity by providing gene expression data at single-cell resolution, uncovering insights into rare cell populations, cell-cell interactions, and gene regulation.
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scLong: A Billion-Parameter Foundation Model for Capturing Long-Range Gene Context in Single-Cell Transcriptomics

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By Ding Bai, Shentong Mo, Ruiyi Zhang, Yingtao Luo
| BioRxiv
Abstract Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of cellular heterogeneity by providing gene expression data at single-cell resolution, uncovering insights into rare cell populations, cell-cell interactions, and gene regulation.
Open in Who Shared Issue with article?

Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE)

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By Yuntian Chen, Qiang Liu, Hao Xu, Yingtao Luo
| Physical Review B
Abstract Partial differential equations (PDEs) are concise and understandable representations of domain knowledge, which are essential for deepening our understanding of physical processes and predicting future responses. However, the PDEs of many real-world problems are uncertain, which calls for PDE discovery. We propose the symbolic genetic algorithm to discover open-form PDEs (SGA-PDE) directly from data without prior knowledge about the equation structure.
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