Jingye Yang

Jun 13, 2024 | biorxiv.org | Andy Wang |Cong Liu |Jingye Yang |Chunhua Weng

AbstractObjective: We aim to develop a novel method for rare disease concept normalization by fine-tuning Llama 2, an open-source large language model (LLM), using a domain-specific corpus sourced from the Human Phenotype Ontology (HPO). Methods: We developed an in-house template-based script to generate two corpora for fine-tuning. The first (NAME) contains standardized HPO names, sourced from the HPO vocabularies, along with their corresponding identifiers.

Apr 14, 2024 | biorxiv.org | Andy Wang |Cong Liu |Jingye Yang |Chunhua Weng

AbstractObjective: We aim to develop a novel method for rare disease concept normalization by fine-tuning Llama 2, an open-source large language model (LLM), using a domain-specific corpus sourced from the Human Phenotype Ontology (HPO). Methods: We developed an in-house template-based script to generate two corpora for fine-tuning. The first (NAME) contains standardized HPO names, sourced from the HPO vocabularies, along with their corresponding identifiers.

Apr 1, 2024 | mdpi.com | Jingye Yang |Kenan Li |Yongqiang Liu |Yongfu Zhang

1. IntroductionOne of the biggest issues facing humanity in the twenty-first century is climate warming, and addressing climate change has drawn attention from all throughout the world. The IPCC report from 2021 states that carbon dioxide emissions are the main cause of the unmistakable warming caused by human activity []. Cities with high population densities are a significant contributor to high energy use and carbon emissions.

Feb 27, 2024 | onlinelibrary.wiley.com | Jingye Yang |Yu Xiong |Xiaoyan Zhu |Qingxiang Lu

CONFLICT OF INTEREST STATEMENT The authors declare no conflicts of interest. Supporting Information Filename Description ceo14370-sup-0001-Supinfo.docWord document, 66 KB Data S1. Supporting Information. ceo14370-sup-0002-FigureS1.tifTIFF image, 14.7 MB FIGURE S1: Clinical manifestations of the RP-11 patient III-3.

Dec 29, 2023 | biorxiv.org | Andy Wang |Cong Liu |Jingye Yang |Chunhua Weng

AbstractObjective: We aim to develop a solution for rare disease concept normalization based on fine-tuning LLaMA 2, an open-source large language model (LLM), using a domain-specific corpus. Methods and Materials: We fine-tuned four LLaMA2 models, each comprising seven billion parameters, using sentences incorporating clinical concepts from the HPO and OMIM vocabularies.