ArticleBioinformatics advances2026
CIViC MCP: integrating large language models with the Clinical Interpretations of Variants in Cancer.
Article in Bioinformatics advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Searching the druggable genome using large language models.Bioinformatics (Oxford, England) · 2026Article
- Searching the Druggable Genome using Large Language Models.bioRxiv : the preprint server for biology · 2026Article
Corrections and comments
- Update of
Authors and funding
15 authors.
Funding
Abstract
Motivation: The Clinical Interpretations of Variants in Cancer (CIViC) knowledgebase provides a community-driven, open-source platform for discussing the biological and clinical significance of molecular variants in cancer. Large language models (LLMs) offer a natural-language interface to CIViC, enabling users to discover and summarize curated oncology knowledge, but LLMs cannot guarantee coverage of specialized, rapidly updated resources like CIViC from pretraining alone. Results: To enable users to make complex connections between CIViC information, we developed the CIViC Model Context Protocol (MCP) server. It allows users to interface with the CIViC API through natural language via LLMs. This facilitates the rapid summarization of expertly curated cancer variant interpretations. Our benchmark shows the server improves the accuracy of clinical significance retrieval while avoiding the far greater latency of GPT-5's Agent Mode, which navigates the web by simulating human inputs. Availability and implementation: The CIViC MCP server is detailed at https://github.com/griffithlab/civic-mcp-server. The repository includes instructions for accessing the server through the Claude desktop app (our recommended approach) and hosting it locally with GPT-5, as well as a Python script for directly querying the MCP server. We also provide an MCP-supported chatbot for CIViC users at https://civicdb.org/mcp-chat.
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.