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Researchers have developed scGNN+, a tool that adapts ChatGPT for seamless tutorial and code optimization. The scGNN+ workflow utilizes dual GPT-4 engines (Duo-GPT) to translate user queries and tutorials into executable commands and code, outperforming single GPT models in code localization and customization tasks. This development aims to improve the accuracy of code generation and provide clear explanations for the generated code. The tool is developed with strict code standards within the fed code for both the ScGNN model and analysis procedure pipeline codes.
Very excited to share our work on building a benchmark of all classes of variation based on the large CEPH-1463 pedigree sequenced with all the technologies. A large team effort! https://t.co/QW9hlpXxwr
🚨New Preprint Alert: Our work led by @JinghuiLi14637 is up on bioRxiv. We re-analyzed large maps of trans-pQTLs, and found that protein–protein interactions shape trans-regulatory impact of genetic variation on protein expression and complex traits. A brief thread🧵👇 https://t.co/tuESDo3ZSk
CREATE: cell-type-specific cis-regulatory elements identification via discrete embedding https://t.co/dQ04UbvCez #biorxiv_bioinfo
