Unpaywall
open access
Deciphering sepsis molecular subtypes using large-scale data to identify subtype-specific drug repurposing
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Presepsin in the Diagnosis of Sepsis in Critically Ill Patients#994351
- Using interpretable machine learning to analyze the trajectory changes of serum albumin to predict the mortality rate of sepsis: a cohort study based on MIMIC-IV.#1097879
- Early recognition of sepsis-associated acute kidney injury based on BioBERT-BPNN multimodal clinical data analysis.#785661
- Early recognition of sepsis-associated acute kidney injury based on BioBERT-BPNN multimodal clinical data analysis#686595
- Explainable machine learning using urinary metabolomics to predict pediatric sepsis-associated acute kidney injury: a two-center prospective observational study.#136113
Record · ID 145668
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive