Wangjie Zheng

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.

Mar 3, 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.

Dec 8, 2023 | biorxiv.org | Tianyu Liu |Tianqi Chen |Xiao Luo |Wangjie Zheng

Abstract Different from a recent approach to building the Foundation Models (FMs) for analyzing single-cell data based on the pre-training and fine-tuning framework, in this manuscript, we extend the concept from GenePT {chen2023genept} and propose a novel approach to leverage the advantages from Large Language Models (LLMs) to formalize a foundation model for single-cell data analysis, known as scELMo. We utilize LLMs like GPT 3.5 as a generator for both the description of metadata...