🔥 Excited to share that #paper2agent is published in @Nature today! Papers have long been the primary format for communicating scientific knowledge, but they remain static. Putting that knowledge to work requires connecting findings to data, navigating supplementary materials, and adapting methods to new questions - effort repeated by each new reader. We introduce Paper2Agent, a multi-agent framework that automatically turns research papers into virtual authors. Paper2Agent turns a paper’s manuscript, code, data, and supplements into an MCP server that any AI agent can access, with automated testing and iterative refinement in an agentic loop. The paper then becomes a virtual author you can talk to: trace claims to evidence, analyze your own data, and collaborate with other papers’ virtual authors Agentifying a paper turns it from something people read into something people and AI agents can discover and build on - providing the context needed to interpret its findings and reuse its methods reliably. We tested how reliably these agents put papers to use. Across multiple benchmarks, agents created by Paper2Agent outperformed baselines such as Claude Code working directly with papers' PDFs and code repositories. Once papers become virtual authors, they can collaborate - much like human researchers do. In one case study, agents built from AlphaGenome and two large-scale genetic perturbation studies worked together to propose a new computational approach for integrating evidence across different perturbation datasets. By connecting predictions from one paper with experimental data from others, they helped pinpoint a likely causal gene for psoriasis. We hope Paper2Agent makes scientific knowledge easier to access, reuse, and build on - a first step toward a future where millions of paper agents proactively collaborate with human researchers and one another to advance discovery. 🤖Talk to the virtual author for Paper2Agent itself: paper2agent.ai 📎Paper: nature.com/articles/s41586-0… 💻Code: github.com/jmiao24/Paper2Age… VERY grateful to work with this incredible team: @james_y_zou, @jkpritch, Yaohui, and Joe!

We showed that polygenic components of treatment efficacy capture far more variation than individual variants, but it's hard to detect them from treatment samples alone (too small). Need transfer learning from large GWAS to pull it out. medrxiv.org/content/10.1101/…

Building on POP-GWAS, we have developed a new method called PSPS, designed for task-agnostic, ML-assisted statistical inference across a wide range of tasks. PSPS was recently accepted at @NeurIPSConf. Paper: arxiv.org/abs/2405.20039 Code: github.com/qlu-lab/psps

I will be on the academic job market this year and looking forward to chatting at ASHG @GeneticsSociety in Denver and @NeurIPSConf in Vancouver!

Paper: rdcu.be/dVCma Code: github.com/qlu-lab/POP-TOOLS GWAS summary statistics for DXA-derived bone mineral density across 14 skeletal sites: qlu-lab.org/data.html Shiny app for power and sample size calculations of ML-assisted GWAS: jmiao24.shinyapps.io/pop-gwa…

Excited to see our POP-GWAS paper out now in @NatureGenet for valid and powerful GWAS on ML-predicted outcomes. Key features include: + Valid and powerful inference for any ML prediction models. + Requires only GWAS summary statistics as input and is computationally efficient.