Tianqi Chen

Nov 27, 2024 | cell.com | Dongping Li |Zhelin Zhang |Yifeng Qiu |Chunxiao Yu |Yao Yu |Tianqi Chen | +11 more

Keywordszinccholestasisgut microbiotaBlautia productahistidine ammonia-lyasep-coumaric acidGSDMEpyroptosisGet full text accessLog in, subscribe or purchase for full access. References1. Hirschfield, G.M. ∙ Heathcote, E.J. ∙ Gershwin, M.E.Pathogenesis of Cholestatic Liver Disease and Therapeutic ApproachesGastroenterology. 2010; 139:1481-14962. Yokoda, R.T. ∙ Rodriguez, E.A.Review: Pathogenesis of cholestatic liver diseasesWorld J. Hepatol. 2020; 12:423-4353. Jansen, P.L.M. ∙ Ghallab, A. ∙ Vartak, N. ...

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.

Jun 28, 2024 | pubs.rsc.org | Zhe Zhang |Shaohui Yuan |Tianqi Chen |Jia Wang

Rational Design of Flexible-linked 3D Dimeric Acceptors for Stable Organic Solar Cells Demonstrating 19.2% Efficiency Organic solar cells; 3D dimeric acceptors; flexible linker; power conversion efficiency; stability. You have access to this article This article has not yet been cited.

May 2, 2024 | octo.ai | Luis Ceze |Jason Knight |Tianqi Chen

GenAI apps are changing the world. We’ve seen through our customers how new apps are simplifying how users get new information, interact with services, and unlock creative potential. Even as organizations explore the newest models and latest capabilities, they are acutely aware of the resource impact of the success of these applications. Builders want efficiency, customizability and reliability, as they build for this growing demand.

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.