
Tianyu Liu
Articles
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Jan 22, 2025 |
advanced.onlinelibrary.wiley.com | Zhuo Chen |Engineering Taiyuan |Nano-Bionics Chinese Academy |Tianyu Liu
1 Introduction Organic solar cells (OSCs) are attracting significant interest for their advantages of being light, flexible, semi-transparent, and Roll-to-Roll (R2R) fabrication compatible.[1-4] Moreover, the development of non-fullerene acceptors has led to a significant increase in the power conversion efficiency (PCE) of single-junction OSCs, with the newest PCE exceeding 20.2%.[5] Flexible organic solar cells (FOSCs) have great potential for applications in wearable devices, the Internet...
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Dec 10, 2024 |
biorxiv.org | Tianyu Liu |Yuge Wang |Hongyu Li |Hongyu Zhao
AbstractFoundation Models (FMs) have made significant strides in both industrial and scientific domains. In this paper, we evaluate the performance of FMs for single-cell sequencing data analysis through comprehensive experiments across eight downstream tasks pertinent to single-cell data.
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Nov 21, 2024 |
biorxiv.org | Tianyu Liu |Xiao Luo |Yijia Xiao |Hongyu Zhao
AbstractComputational methods should be accurate and robust for tasks in biology and medicine, especially when facing different types of attacks, defined as perturbations of benign data that can cause a significant drop in method performance. Therefore, there is a need for robust models that can defend attacks. In this manuscript, we propose a novel framework named RobustCell to analyze attack-defense methods in single-cell and spatial transcriptomic data analysis.
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Nov 21, 2024 |
biorxiv.org | Tianyu Liu |Jia Zhao |Hongyu Zhao
AbstractSingle-cell Multi-modal Data Integration has been an area of active research in recent years. However, it is difficult to unify the integration process of different omics in a pipeline, and evaluate the contributions of data integration.
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Nov 18, 2024 |
biorxiv.org | Tianyu Liu |Tianqi Chen |Wangjie Zheng |Xiao Luo
AbstractVarious Foundation Models (FMs) have been built based on the pre-training and fine-tuning framework to analyze single-cell data with different degrees of success. In this manuscript, we propose a method named scELMo (Single-cell Embedding from Language Models), to analyze single-cell data that utilizes Large Language Models (LLMs) as a generator for both the description of metadata information and the embeddings for such descriptions.
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